This document reviews recent trends in texture classification methods. It summarizes that signal processing feature extraction methods like Gabor filters and wavelets have become popular due to providing higher accuracy, though older methods like gray level co-occurrence matrices are still used. For classification, nearest neighbor algorithms remain common due to simplicity, while support vector machines have increased in usage. Brodatz texture datasets are most frequently employed despite limitations, while other datasets see less use. In general, research emphasizes accuracy over speed, utilizing more complex feature extraction and classification algorithms.
06 9237 it texture classification based edit putriIAESIJEECS
Automatic inspection systems become more importance for industries with high productive plans especially in texture industry. A novel approach to Local Binary Pattern (LBP) feature for texture classification is proposed in this system. At the first, the proposed Empirical Wavelet Transform (EWT) based texture classification is tested on gray scale and color images by using Brodatz texture images. The gray scale and color image is decomposed by EWT at 2 and 3 level of decomposition. LBP features are calculated for each empirical transformed image. Extracted features are given as input to the classification stage. K-NN classifier is used for classification stage. The result of the proposed system gives satisfactory classification accuracy of over 98% for all types of images.
An efficient fuzzy classifier with feature selection basedssairayousaf
This document presents an efficient fuzzy classifier with feature selection capabilities. A fuzzy entropy measure is used to partition the input feature space into non-overlapping decision regions and to select relevant features. Fuzzy entropy evaluates the information of pattern distribution in the pattern space. The decision regions do not overlap, reducing computational complexity and load. Classification speed is extremely fast while still achieving good performance by correctly determining decision region boundaries. Feature selection via fuzzy entropy reduces dimensionality by discarding noisy, redundant, and unimportant features. The proposed classifier is applied to two databases with good classification results, demonstrating its effectiveness.
Texture Segmentation Based on Multifractal Dimensionijsc
Texture segmentation can be considered the most important problem, since human can distinguish different
textures quit easily, but the automatic segmentation is quit complex and it is still an open problem for
research. In this paper focus on implement novel supervised algorithm for multitexture segmentation and
this algorithm based on blocking procedure where each image divide into block (16×16 pixels) and extract
vector feature for each block to classification these block based on these feature. These feature extract
using Box Counting Method (BCM). BCM generate single feature for each block and this feature not
enough to characterize each block ,therefore, must be implement algorithm provide more than one slide for
the image based on new method produce multithresolding, after this use BCM to generate single feature for
each slide.
Texture Images Classification using Secant Lines Segments Histogramijtsrd
Texture classification is the process to classify different textures from the given images. The aim of texture classification is to classify the category of a texture image. To design an effective algorithm for texture classification, it is essential to find a set of texture features with good discriminating power. This paper presents a texture classification system using secant lines segments histogram and Euclidean Distance. Secant lines segments histogram is used to generate the features from texture images as a histogram. These features offer a better discriminating strategy for texture classification. These features are first used for training and later on for classifying the texture images. Euclidean Distance is used for distinguishing each of the known categories for classification. Ei Phyu Win | Mie Mie Tin | Pyae Phyo Thu "Texture Images Classification using Secant Lines Segments Histogram" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27984.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/27984/texture-images-classification-using-secant-lines-segments-histogram/ei-phyu-win
Wavelet based histogram method for classification of textuIAEME Publication
This document summarizes a research paper that proposes a new method called Wavelet based Histogram on Texton Patterns (WHTP) for classifying textures. The method applies a discrete wavelet transform to texture images and extracts texton frequencies from the approximation and detail subbands at different scales. It calculates texton frequencies for original images and wavelet-transformed images. Combining these texton frequencies improves classification success rates when distinguishing between various types of stone textures. The paper aims to improve on other texture classification methods by incorporating spatial information using textons in the wavelet domain. An experimental evaluation finds the proposed WHTP method achieves more accurate classification of stone textures compared to other approaches.
Optimized Neural Network for Classification of Multispectral ImagesIDES Editor
This document summarizes an article that proposes using a multiobjective particle swarm optimization (MOPSO) approach to optimize the structure of an artificial neural network for classifying multispectral satellite images. Specifically, the MOPSO is used to simultaneously select the most discriminative spectral bands from the available options and determine the optimal number of nodes in the hidden layer of the neural network. The MOPSO approach is compared to traditional classifiers like maximum likelihood classification and Euclidean classifiers. The results show that the MOPSO-optimized neural network approach provides superior performance for remote sensing image classification problems.
FUZZY SET THEORETIC APPROACH TO IMAGE THRESHOLDINGIJCSEA Journal
Thresholding is a fast, popular and computationally inexpensive segmentation technique that is always critical and decisive in some image processing applications. The result of image thresholding is not always satisfactory because of the presence of noise and vagueness and ambiguity among the classes. Since the theory of fuzzy sets is a generalization of the classical set theory, it has greater flexibility to capture faithfully the various aspects of incompleteness or imperfectness in information of situation. To overcome this problem, in this paper we proposed a two-stage fuzzy set theoretic approach to image thresholding utilizing the measure of fuzziness to evaluate the fuzziness of an image and to determine an adequate threshold value. At first, images are preprocessed to reduce noise without any loss of image details by fuzzy rule-based filtering and then in the final stage a suitable threshold is determined with the help of a fuzziness measure as a criterion function. Experimental results on test images have demonstrated the effectiveness of this method.
MAGNETIC RESONANCE BRAIN IMAGE SEGMENTATIONVLSICS Design
Segmentation of tissues and structures from medical images is the first step in many image analysis applications developed for medical diagnosis. With the growing research on medical image segmentation, it is essential to categorize the research outcomes and provide researchers with an overview of the existing segmentation techniques in medical images. In this paper, different image segmentation methods applied on magnetic resonance brain images are reviewed. The selection of methods includes sources from image processing journals, conferences, books, dissertations and thesis. The conceptual details of the methods are explained and mathematical details are avoided for simplicity. Both broad and detailed categorizations of reviewed segmentation techniques are provided. The state of art research is provided with emphasis on developed techniques and image properties used by them. The methods defined are not always mutually independent. Hence, their inter relationships are also stated. Finally, conclusions are drawn summarizing commonly used techniques and their complexities in application.
06 9237 it texture classification based edit putriIAESIJEECS
Automatic inspection systems become more importance for industries with high productive plans especially in texture industry. A novel approach to Local Binary Pattern (LBP) feature for texture classification is proposed in this system. At the first, the proposed Empirical Wavelet Transform (EWT) based texture classification is tested on gray scale and color images by using Brodatz texture images. The gray scale and color image is decomposed by EWT at 2 and 3 level of decomposition. LBP features are calculated for each empirical transformed image. Extracted features are given as input to the classification stage. K-NN classifier is used for classification stage. The result of the proposed system gives satisfactory classification accuracy of over 98% for all types of images.
An efficient fuzzy classifier with feature selection basedssairayousaf
This document presents an efficient fuzzy classifier with feature selection capabilities. A fuzzy entropy measure is used to partition the input feature space into non-overlapping decision regions and to select relevant features. Fuzzy entropy evaluates the information of pattern distribution in the pattern space. The decision regions do not overlap, reducing computational complexity and load. Classification speed is extremely fast while still achieving good performance by correctly determining decision region boundaries. Feature selection via fuzzy entropy reduces dimensionality by discarding noisy, redundant, and unimportant features. The proposed classifier is applied to two databases with good classification results, demonstrating its effectiveness.
Texture Segmentation Based on Multifractal Dimensionijsc
Texture segmentation can be considered the most important problem, since human can distinguish different
textures quit easily, but the automatic segmentation is quit complex and it is still an open problem for
research. In this paper focus on implement novel supervised algorithm for multitexture segmentation and
this algorithm based on blocking procedure where each image divide into block (16×16 pixels) and extract
vector feature for each block to classification these block based on these feature. These feature extract
using Box Counting Method (BCM). BCM generate single feature for each block and this feature not
enough to characterize each block ,therefore, must be implement algorithm provide more than one slide for
the image based on new method produce multithresolding, after this use BCM to generate single feature for
each slide.
Texture Images Classification using Secant Lines Segments Histogramijtsrd
Texture classification is the process to classify different textures from the given images. The aim of texture classification is to classify the category of a texture image. To design an effective algorithm for texture classification, it is essential to find a set of texture features with good discriminating power. This paper presents a texture classification system using secant lines segments histogram and Euclidean Distance. Secant lines segments histogram is used to generate the features from texture images as a histogram. These features offer a better discriminating strategy for texture classification. These features are first used for training and later on for classifying the texture images. Euclidean Distance is used for distinguishing each of the known categories for classification. Ei Phyu Win | Mie Mie Tin | Pyae Phyo Thu "Texture Images Classification using Secant Lines Segments Histogram" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27984.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/27984/texture-images-classification-using-secant-lines-segments-histogram/ei-phyu-win
Wavelet based histogram method for classification of textuIAEME Publication
This document summarizes a research paper that proposes a new method called Wavelet based Histogram on Texton Patterns (WHTP) for classifying textures. The method applies a discrete wavelet transform to texture images and extracts texton frequencies from the approximation and detail subbands at different scales. It calculates texton frequencies for original images and wavelet-transformed images. Combining these texton frequencies improves classification success rates when distinguishing between various types of stone textures. The paper aims to improve on other texture classification methods by incorporating spatial information using textons in the wavelet domain. An experimental evaluation finds the proposed WHTP method achieves more accurate classification of stone textures compared to other approaches.
Optimized Neural Network for Classification of Multispectral ImagesIDES Editor
This document summarizes an article that proposes using a multiobjective particle swarm optimization (MOPSO) approach to optimize the structure of an artificial neural network for classifying multispectral satellite images. Specifically, the MOPSO is used to simultaneously select the most discriminative spectral bands from the available options and determine the optimal number of nodes in the hidden layer of the neural network. The MOPSO approach is compared to traditional classifiers like maximum likelihood classification and Euclidean classifiers. The results show that the MOPSO-optimized neural network approach provides superior performance for remote sensing image classification problems.
FUZZY SET THEORETIC APPROACH TO IMAGE THRESHOLDINGIJCSEA Journal
Thresholding is a fast, popular and computationally inexpensive segmentation technique that is always critical and decisive in some image processing applications. The result of image thresholding is not always satisfactory because of the presence of noise and vagueness and ambiguity among the classes. Since the theory of fuzzy sets is a generalization of the classical set theory, it has greater flexibility to capture faithfully the various aspects of incompleteness or imperfectness in information of situation. To overcome this problem, in this paper we proposed a two-stage fuzzy set theoretic approach to image thresholding utilizing the measure of fuzziness to evaluate the fuzziness of an image and to determine an adequate threshold value. At first, images are preprocessed to reduce noise without any loss of image details by fuzzy rule-based filtering and then in the final stage a suitable threshold is determined with the help of a fuzziness measure as a criterion function. Experimental results on test images have demonstrated the effectiveness of this method.
MAGNETIC RESONANCE BRAIN IMAGE SEGMENTATIONVLSICS Design
Segmentation of tissues and structures from medical images is the first step in many image analysis applications developed for medical diagnosis. With the growing research on medical image segmentation, it is essential to categorize the research outcomes and provide researchers with an overview of the existing segmentation techniques in medical images. In this paper, different image segmentation methods applied on magnetic resonance brain images are reviewed. The selection of methods includes sources from image processing journals, conferences, books, dissertations and thesis. The conceptual details of the methods are explained and mathematical details are avoided for simplicity. Both broad and detailed categorizations of reviewed segmentation techniques are provided. The state of art research is provided with emphasis on developed techniques and image properties used by them. The methods defined are not always mutually independent. Hence, their inter relationships are also stated. Finally, conclusions are drawn summarizing commonly used techniques and their complexities in application.
This document proposes a new method for image segmentation using histogram thresholding and hierarchical cluster analysis. The method develops a dendrogram (hierarchical tree) of gray levels in an image histogram based on a similarity measure involving the inter-class variance of clusters to be merged and the intra-class variance of the new merged cluster. By iteratively merging the most similar clusters in a bottom-up approach, the dendrogram yields a clear separation of object and background pixels, providing robust threshold estimates. The method can be extended to multi-level thresholding by terminating the clustering at different levels in the dendrogram. Experiments show the method outperforms Otsu's and Kwon's thresholding methods.
An Analysis and Comparison of Quality Index Using Clustering Techniques for S...CSCJournals
This document presents a proposed methodology for microarray image segmentation using clustering techniques. The methodology involves three main steps: preprocessing, gridding, and segmentation. Segmentation is performed using an enhanced fuzzy c-means clustering algorithm (EFCMC) that uses neighborhood pixel information and gray levels. EFCMC can accurately detect absent spots and is tolerant to noise. The methodology is tested on real microarray images and its segmentation quality is assessed using a quality index. Results show EFCMC improves the quality index compared to k-means clustering and fuzzy c-means clustering.
Survey on Segmentation of Partially Overlapping ObjectsIRJET Journal
This document summarizes several existing methods for segmenting partially overlapping objects in digital images. It discusses challenges in segmenting overlapping objects and different approaches researchers have used, including watershed-based methods, graph cuts algorithms, active shape models, and level set methods. The goal of segmentation is to partition an image into meaningful regions to analyze objects and boundaries. Efficient segmentation of overlapping objects remains an important challenge in image processing.
Image Steganography Using Wavelet Transform And Genetic AlgorithmAM Publications
This paper presents the application of Wavelet Transform and Genetic Algorithm in a novel
steganography scheme. We employ a genetic algorithm based mapping function to embed data in Discrete Wavelet
Transform coefficients in 4x4 blocks on the cover image. The optimal pixel adjustment process is applied after
embedding the message. We utilize the frequency domain to improve the robustness of steganography and, we
implement Genetic Algorithm and Optimal Pixel Adjustment Process to obtain an optimal mapping function to
reduce the difference error between the cover and the stego-image, therefore improving the hiding capacity with
low distortions. Our Simulation results reveal that the novel scheme outperforms adaptive steganography technique
based on wavelet transform in terms of peak signal to noise ratio and capacity, 39.94 dB and 50% respectively.
Farsi character recognition using new hybrid feature extraction methodsijcseit
Identification of visual words and writings has long been one of the most essential and the most attractive
operations in the field of image processing which has been studied since the last few decades and includes
security, traffic control, fields of psychology, medicine, and engineering, etc. Previous techniques in the
field of identification of visual writings are very similar to each other for the most parts of their analysis,
and depending on the needs of the operational field have presented different feature extraction. Changes in
style of writing and font and turns of words and other issues are challenges of characters identifying
activity. In this study, a system of Persian character identification using independent orthogonal moment
that is Zernike Moment and Fourier-Mellin Moment has been used as feature extraction technique. The
values of Zernike Moments as characteristics independent of rotation have been used for classification
issues in the past and each of their real and imaginary components have been neglected individually and
with the phase coefficients, each of them will be changed by rotation. In this study, Zernike and Fourier-
Mellin Moments have been investigated to detect Persian characters in noisy and noise-free images. Also,
an improvement on the k-Nearest Neighbor (k-NN) classifier is proposed for character recognition. Using
the results comparison of the proposed method with current salient methods such as Back Propagation
(BP) and Radial Basis Function (RBF) neural networks in terms of feature extraction in words, it has been
shown that on the Hoda database, the proposed method reaches an acceptable detection rate (96/5%).
Number of Iteration Analysis for Complex FSS Shape Using GA for Efficient ESGjournalBEEI
ESG stand for Energy-Saving Glass is a special shielded glass with a metallic oxide layer to abuse undesirable of infrared and ultraviolet radiation into construction assemblies like our home. Firstly, different number of the iteration is the main thing to study a performance of the frequency selective surface shape using genetic algorithm (GA) for efficient energy saving glass (ESG). Three different values for the number of iterations were taken that is 1500, 2000 1nd 5000. Before that, the response of this complex FSS shape on incident electromagnetic wave with different symmetry shape are investigating. Three of them are no symmetrical shape, ¼ symmetrical shape, and 1/8 symmetrical shape. The 1500 number simulation considered about 89.000 per second, compared with 2000 iteration and 5000 iterations had consumed 105.09 per second and 196.00 per second, respectively. For 1/8 symmetry complex FSS shape, it demonstrations the improved performance of transmission loss at 1.2 GHz with - 40 dB. A 2 dB of transmission loss is achieved at WLAN application of 2.45 GHz with 0°, 30°, and 45° incidence angle shows
Review paper on segmentation methods for multiobject feature extractioneSAT Journals
Abstract Feature extraction and representation plays a vital role in multimedia processing. It is still a challenge in computer vision system to extract ideal features that represents intrinsic characteristics of an image. Multiobject feature extraction system means a system that can extract features and locations of multiple objects in an image. In this paper we have discuss various methods to extract location and features of multiple objects and describe a system that can extract locations and features of multiple objects in an image by implementing an algorithm as hardware logic on a field-programmable gate array-based platform. There are many multiobject extraction methods which can be use for image segmentation based on motion, color intensity and texture. By calculating zeroth and first order moments of objects it is possible to obtain locations and sizes of multiple objects in an image. Keywords: multiobject extraction, image segmentation
Particle Swarm Optimization for Nano-Particles Extraction from Supporting Mat...CSCJournals
This document summarizes a study that uses Particle Swarm Optimization (PSO) for automatic segmentation of nano-particles in Transmission Electron Microscopy (TEM) images. PSO is applied to specify local and global thresholds for segmentation by treating image entropy as a minimization problem. Results show the PSO method improves over previous techniques by reducing incorrect characterization of nano-particles in images affected by liquid concentrations or supporting materials, with up to a 27% reduction in errors. Compared to manual characterization, PSO provides comparable particle counting with higher computational efficiency suitable for real-time analysis.
Orientation Spectral Resolution Coding for Pattern RecognitionIOSRjournaljce
In the approach of pattern recognition, feature descriptions are of greater importance. Features are represented in spatial domain and transformed domain. Wherein, spatial domain features are of lower representation, transformed domains are finer and more informative. In the transformed domain representation, features are represented using spectral coding using advanced transformation technique such as wavelet transformation. However, the feature extraction approach considers the band coefficients; the orientation variation is not considered. In this paper towards inherent orientation variation among each spectral band is derived, and the approach of orientation filtration is made for effective feature representation. The obtained result illustrates an improvement in the recognition accuracy, in comparison to conventional retrieval system.
OBIA on Coastal Landform Based on Structure Tensor csandit
This paper presents the OBIA method based on structure tensor to identify complex coastal
landforms. That is, develop Hessian matrix by Gabor filtering and calculate multiscale structure
tensor. Extract edge information of image from the trace of structure tensor and conduct
watershed segment of the image. Then, develop texons and create texton histogram. Finally,
obtain the final results by means of maximum likelihood classification with KL divergence as
the similarity measurement. The study findings show that structure tensor could obtain
multiscale and all-direction information with small data redundancy. Moreover, the method
described in the current paper has high classification accuracy
This paper proposes a new fuzzy similarity measure called Fuzzy Monotonic Inclusion (FMI) to measure similarity between images for image retrieval systems. The FMI approach segments images into regions, extracts features for each region, and maps the features into a fuzzy similarity model based on fuzzy inclusion. Experimental results on the Label Me image dataset show the FMI approach achieves higher precision than other methods like Unified Feature Matching and Fuzzy Histogram in identifying images by semantic class.
Object Recogniton Based on Undecimated Wavelet TransformIJCOAiir
Object Recognition (OR) is the mission of finding a specified object in an image or video sequence
in computer vision. An efficient method for recognizing object in an image based on Undecimated Wavelet
Transform (UWT) is proposed. In this system, the undecimated coefficients are used as features to recognize the
objects. The given original image is decomposed by using the UWT. All coefficients are taken as features for
the classification process. This method is applied to all the training images and the extracted features of
unknown object are used as an input to the K-Nearest Neighbor (K-NN) classifier to recognize the object. The
assessment of the system is agreed on using Columbia Object Image Library Dataset (COIL-100) database.
This document proposes three methods to improve semantic segmentation using self-supervised depth estimation from unlabeled image sequences:
1. It transfers knowledge from features learned during self-supervised depth estimation to semantic segmentation through multi-task learning.
2. It introduces a new data augmentation technique called DepthMix which blends images and labels according to the geometry of the scene from depth estimation, generating fewer artifacts than prior methods.
3. It proposes an automatic data selection method to select the most useful unlabeled samples for annotation, driven by diversity and uncertainty criteria evaluated using depth estimation as a proxy task, avoiding the need for human annotation in active learning loops.
Computational Intelligence Approach for Predicting the Hardness Performances ...Waqas Tariq
This paper presents a computational approach on predicting of hardness performances for Titanium Aluminium Nitride (TiA1N) coating process. A new application in predicting the hardness performances of TiA1N coatings using a method called Support Vector Machine (SVM) and Artificial Neural Network (ANN) is implemented. TiAlN coatings are usually used in high-speed machining due to its excellent properties in surface hardness and wear resistance. Physical Vapor Deposition (PVD) magnetron sputtering process has been used to produce the TiA1N coatings. Based on the experimental dataset of previous work, the SVM and ANN model is used in predicting the hardness of TiA1N coatings. The influential factors of three coating process parameter namely substrate sputtering power, substrate bias voltage and substrate temperature were selected as input while the output parameter is the hardness. The results of proposed SVM and ANN models are compared to the experimental result and the hybrid RSM-Fuzzy model from previous work. The comparisons of SVM and ANN models against hybrid RSM-Fuzzy were based on predictive performances in order to obtain the most accurate model for prediction of hardness in TiA1N coating. In terms of predictive performance evaluation, four performances matrix were applied that are percentage error, mean square error (MSE), co-efficient determination (R 2) and model accuracy. The result has proved that the proposed SVM model shows the better result compared to the ANN and hybrid RSM-fuzzy model. The good performances of the results obtained by the SVM method shows that this method can be applied for prediction of hardness performances in TiA1N coating process with better predictive performances compared to ANN and hybrid RSM-Fuzzy.
A New Approach for Segmentation of Fused Images using Cluster based ThresholdingIDES Editor
This paper proposes the new segmentation technique
with cluster based method. In this, the multi source medical
images like MRI (Magnetic Resonance Imaging), CT
(computed tomography) & PET (positron emission
tomography) are fused and then segmented using cluster based
thresholding approach. The edge details of an image have
become an essential technique in clinical and researchoriented
applications. The more edge details of the fused image
have obtainable with this method. The objective of the
clustering process is to partition a fused image coefficients
into a number of clusters having similar features. These
features are useful to generate the threshold value for further
segmentation of fused image. Finally the segmented output
is compared with standard FCM method and modified Otsu
method. Experimental results have shown that the proposed
cluster based thresholding method is able to effectively extract
important edge details of fused image.
Kernel based similarity estimation and real time tracking of movingIAEME Publication
This document discusses kernel-based mean shift algorithm for real-time object tracking. It presents the following:
1) The algorithm uses kernel density estimation to calculate the similarity between a target model and candidate windows, using the Bhattacharyya coefficient. 2) It can successfully track objects moving uniformly at slow speeds but struggles with fast or non-uniform motion, or changes in scale. 3) The algorithm was tested on video streams and could track objects moving slowly but failed for fast or irregular motion. Adaptive target windows are needed to handle changes in scale.
National Flags Recognition Based on Principal Component Analysisijtsrd
Recognizing an unknown flag in a scene is challenging due to the diversity of the data and to the complexity of the identification process. And flags are associated with geographical regions, countries and nations. But flag identification of different countries is a challenging and difficult task. Recognition of an unknown flag image in a scene is challenging due to the diversity of the data and to the complexity of the identification process. The aim of the study is to propose a feature extraction based recognition system for Myanmar's national flag. Image features are acquired from the region and state of flags which are identified by using principal component analysis PCA . PCA is a statistical approach used for reducing the number of features in National flags recognition system. Soe Moe Myint | Moe Moe Myint | Aye Aye Cho "National Flags Recognition Based on Principal Component Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26775.pdfPaper URL: https://www.ijtsrd.com/other-scientific-research-area/other/26775/national-flags-recognition-based-on-principal-component-analysis/soe-moe-myint
Flower Classification Using Neural Network Based Image ProcessingIOSR Journals
Abstract: In this paper, it is proposed to have a method for classification of flowers using Artificial Neural Network (ANN) classifier. The proposed method is based on textural features such as Gray level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT). A flower image is segmented using a threshold based method. The data set has different flower images with similar appearance .The database of flower images is a mixture of images taken from World Wide Web and the images taken by us. The ANN has been trained by 50 samples to classify 5 classes of flowers and achieved classification accuracy more than 85% using GLCM features only. Keywords: Artificial Neural Network, DWT, GLCM, Segmentation.
The document summarizes research using Cosmo-SkyMed SAR images to automatically extract features and classify land cover in suburban areas. A neural network classifier achieved over 80% accuracy distinguishing four classes (asphalt, vegetation, trees, manmade structures) using backscatter intensity and GLCM texture features. Ongoing work includes optimizing the algorithm and incorporating information from multiple dates, polarizations and a change detection method.
This document contains descriptions and microscope images of several types of marine microalgae collected from Casco Bay in South Portland, Maine on March 6th and 17th, 2014. It identifies and provides details about the distinctive morphological features of several cennate and pennate diatom species, including Chaetoceros danicus, Chaetoceros debillas, Chaetoceros decipiens, Cylindrotheca closterium, Ditylum brightwellii, and Licmophora flabellata. For each type of algae, the document notes the sample source and magnification used for the accompanying microscope image.
This document proposes a new method for image segmentation using histogram thresholding and hierarchical cluster analysis. The method develops a dendrogram (hierarchical tree) of gray levels in an image histogram based on a similarity measure involving the inter-class variance of clusters to be merged and the intra-class variance of the new merged cluster. By iteratively merging the most similar clusters in a bottom-up approach, the dendrogram yields a clear separation of object and background pixels, providing robust threshold estimates. The method can be extended to multi-level thresholding by terminating the clustering at different levels in the dendrogram. Experiments show the method outperforms Otsu's and Kwon's thresholding methods.
An Analysis and Comparison of Quality Index Using Clustering Techniques for S...CSCJournals
This document presents a proposed methodology for microarray image segmentation using clustering techniques. The methodology involves three main steps: preprocessing, gridding, and segmentation. Segmentation is performed using an enhanced fuzzy c-means clustering algorithm (EFCMC) that uses neighborhood pixel information and gray levels. EFCMC can accurately detect absent spots and is tolerant to noise. The methodology is tested on real microarray images and its segmentation quality is assessed using a quality index. Results show EFCMC improves the quality index compared to k-means clustering and fuzzy c-means clustering.
Survey on Segmentation of Partially Overlapping ObjectsIRJET Journal
This document summarizes several existing methods for segmenting partially overlapping objects in digital images. It discusses challenges in segmenting overlapping objects and different approaches researchers have used, including watershed-based methods, graph cuts algorithms, active shape models, and level set methods. The goal of segmentation is to partition an image into meaningful regions to analyze objects and boundaries. Efficient segmentation of overlapping objects remains an important challenge in image processing.
Image Steganography Using Wavelet Transform And Genetic AlgorithmAM Publications
This paper presents the application of Wavelet Transform and Genetic Algorithm in a novel
steganography scheme. We employ a genetic algorithm based mapping function to embed data in Discrete Wavelet
Transform coefficients in 4x4 blocks on the cover image. The optimal pixel adjustment process is applied after
embedding the message. We utilize the frequency domain to improve the robustness of steganography and, we
implement Genetic Algorithm and Optimal Pixel Adjustment Process to obtain an optimal mapping function to
reduce the difference error between the cover and the stego-image, therefore improving the hiding capacity with
low distortions. Our Simulation results reveal that the novel scheme outperforms adaptive steganography technique
based on wavelet transform in terms of peak signal to noise ratio and capacity, 39.94 dB and 50% respectively.
Farsi character recognition using new hybrid feature extraction methodsijcseit
Identification of visual words and writings has long been one of the most essential and the most attractive
operations in the field of image processing which has been studied since the last few decades and includes
security, traffic control, fields of psychology, medicine, and engineering, etc. Previous techniques in the
field of identification of visual writings are very similar to each other for the most parts of their analysis,
and depending on the needs of the operational field have presented different feature extraction. Changes in
style of writing and font and turns of words and other issues are challenges of characters identifying
activity. In this study, a system of Persian character identification using independent orthogonal moment
that is Zernike Moment and Fourier-Mellin Moment has been used as feature extraction technique. The
values of Zernike Moments as characteristics independent of rotation have been used for classification
issues in the past and each of their real and imaginary components have been neglected individually and
with the phase coefficients, each of them will be changed by rotation. In this study, Zernike and Fourier-
Mellin Moments have been investigated to detect Persian characters in noisy and noise-free images. Also,
an improvement on the k-Nearest Neighbor (k-NN) classifier is proposed for character recognition. Using
the results comparison of the proposed method with current salient methods such as Back Propagation
(BP) and Radial Basis Function (RBF) neural networks in terms of feature extraction in words, it has been
shown that on the Hoda database, the proposed method reaches an acceptable detection rate (96/5%).
Number of Iteration Analysis for Complex FSS Shape Using GA for Efficient ESGjournalBEEI
ESG stand for Energy-Saving Glass is a special shielded glass with a metallic oxide layer to abuse undesirable of infrared and ultraviolet radiation into construction assemblies like our home. Firstly, different number of the iteration is the main thing to study a performance of the frequency selective surface shape using genetic algorithm (GA) for efficient energy saving glass (ESG). Three different values for the number of iterations were taken that is 1500, 2000 1nd 5000. Before that, the response of this complex FSS shape on incident electromagnetic wave with different symmetry shape are investigating. Three of them are no symmetrical shape, ¼ symmetrical shape, and 1/8 symmetrical shape. The 1500 number simulation considered about 89.000 per second, compared with 2000 iteration and 5000 iterations had consumed 105.09 per second and 196.00 per second, respectively. For 1/8 symmetry complex FSS shape, it demonstrations the improved performance of transmission loss at 1.2 GHz with - 40 dB. A 2 dB of transmission loss is achieved at WLAN application of 2.45 GHz with 0°, 30°, and 45° incidence angle shows
Review paper on segmentation methods for multiobject feature extractioneSAT Journals
Abstract Feature extraction and representation plays a vital role in multimedia processing. It is still a challenge in computer vision system to extract ideal features that represents intrinsic characteristics of an image. Multiobject feature extraction system means a system that can extract features and locations of multiple objects in an image. In this paper we have discuss various methods to extract location and features of multiple objects and describe a system that can extract locations and features of multiple objects in an image by implementing an algorithm as hardware logic on a field-programmable gate array-based platform. There are many multiobject extraction methods which can be use for image segmentation based on motion, color intensity and texture. By calculating zeroth and first order moments of objects it is possible to obtain locations and sizes of multiple objects in an image. Keywords: multiobject extraction, image segmentation
Particle Swarm Optimization for Nano-Particles Extraction from Supporting Mat...CSCJournals
This document summarizes a study that uses Particle Swarm Optimization (PSO) for automatic segmentation of nano-particles in Transmission Electron Microscopy (TEM) images. PSO is applied to specify local and global thresholds for segmentation by treating image entropy as a minimization problem. Results show the PSO method improves over previous techniques by reducing incorrect characterization of nano-particles in images affected by liquid concentrations or supporting materials, with up to a 27% reduction in errors. Compared to manual characterization, PSO provides comparable particle counting with higher computational efficiency suitable for real-time analysis.
Orientation Spectral Resolution Coding for Pattern RecognitionIOSRjournaljce
In the approach of pattern recognition, feature descriptions are of greater importance. Features are represented in spatial domain and transformed domain. Wherein, spatial domain features are of lower representation, transformed domains are finer and more informative. In the transformed domain representation, features are represented using spectral coding using advanced transformation technique such as wavelet transformation. However, the feature extraction approach considers the band coefficients; the orientation variation is not considered. In this paper towards inherent orientation variation among each spectral band is derived, and the approach of orientation filtration is made for effective feature representation. The obtained result illustrates an improvement in the recognition accuracy, in comparison to conventional retrieval system.
OBIA on Coastal Landform Based on Structure Tensor csandit
This paper presents the OBIA method based on structure tensor to identify complex coastal
landforms. That is, develop Hessian matrix by Gabor filtering and calculate multiscale structure
tensor. Extract edge information of image from the trace of structure tensor and conduct
watershed segment of the image. Then, develop texons and create texton histogram. Finally,
obtain the final results by means of maximum likelihood classification with KL divergence as
the similarity measurement. The study findings show that structure tensor could obtain
multiscale and all-direction information with small data redundancy. Moreover, the method
described in the current paper has high classification accuracy
This paper proposes a new fuzzy similarity measure called Fuzzy Monotonic Inclusion (FMI) to measure similarity between images for image retrieval systems. The FMI approach segments images into regions, extracts features for each region, and maps the features into a fuzzy similarity model based on fuzzy inclusion. Experimental results on the Label Me image dataset show the FMI approach achieves higher precision than other methods like Unified Feature Matching and Fuzzy Histogram in identifying images by semantic class.
Object Recogniton Based on Undecimated Wavelet TransformIJCOAiir
Object Recognition (OR) is the mission of finding a specified object in an image or video sequence
in computer vision. An efficient method for recognizing object in an image based on Undecimated Wavelet
Transform (UWT) is proposed. In this system, the undecimated coefficients are used as features to recognize the
objects. The given original image is decomposed by using the UWT. All coefficients are taken as features for
the classification process. This method is applied to all the training images and the extracted features of
unknown object are used as an input to the K-Nearest Neighbor (K-NN) classifier to recognize the object. The
assessment of the system is agreed on using Columbia Object Image Library Dataset (COIL-100) database.
This document proposes three methods to improve semantic segmentation using self-supervised depth estimation from unlabeled image sequences:
1. It transfers knowledge from features learned during self-supervised depth estimation to semantic segmentation through multi-task learning.
2. It introduces a new data augmentation technique called DepthMix which blends images and labels according to the geometry of the scene from depth estimation, generating fewer artifacts than prior methods.
3. It proposes an automatic data selection method to select the most useful unlabeled samples for annotation, driven by diversity and uncertainty criteria evaluated using depth estimation as a proxy task, avoiding the need for human annotation in active learning loops.
Computational Intelligence Approach for Predicting the Hardness Performances ...Waqas Tariq
This paper presents a computational approach on predicting of hardness performances for Titanium Aluminium Nitride (TiA1N) coating process. A new application in predicting the hardness performances of TiA1N coatings using a method called Support Vector Machine (SVM) and Artificial Neural Network (ANN) is implemented. TiAlN coatings are usually used in high-speed machining due to its excellent properties in surface hardness and wear resistance. Physical Vapor Deposition (PVD) magnetron sputtering process has been used to produce the TiA1N coatings. Based on the experimental dataset of previous work, the SVM and ANN model is used in predicting the hardness of TiA1N coatings. The influential factors of three coating process parameter namely substrate sputtering power, substrate bias voltage and substrate temperature were selected as input while the output parameter is the hardness. The results of proposed SVM and ANN models are compared to the experimental result and the hybrid RSM-Fuzzy model from previous work. The comparisons of SVM and ANN models against hybrid RSM-Fuzzy were based on predictive performances in order to obtain the most accurate model for prediction of hardness in TiA1N coating. In terms of predictive performance evaluation, four performances matrix were applied that are percentage error, mean square error (MSE), co-efficient determination (R 2) and model accuracy. The result has proved that the proposed SVM model shows the better result compared to the ANN and hybrid RSM-fuzzy model. The good performances of the results obtained by the SVM method shows that this method can be applied for prediction of hardness performances in TiA1N coating process with better predictive performances compared to ANN and hybrid RSM-Fuzzy.
A New Approach for Segmentation of Fused Images using Cluster based ThresholdingIDES Editor
This paper proposes the new segmentation technique
with cluster based method. In this, the multi source medical
images like MRI (Magnetic Resonance Imaging), CT
(computed tomography) & PET (positron emission
tomography) are fused and then segmented using cluster based
thresholding approach. The edge details of an image have
become an essential technique in clinical and researchoriented
applications. The more edge details of the fused image
have obtainable with this method. The objective of the
clustering process is to partition a fused image coefficients
into a number of clusters having similar features. These
features are useful to generate the threshold value for further
segmentation of fused image. Finally the segmented output
is compared with standard FCM method and modified Otsu
method. Experimental results have shown that the proposed
cluster based thresholding method is able to effectively extract
important edge details of fused image.
Kernel based similarity estimation and real time tracking of movingIAEME Publication
This document discusses kernel-based mean shift algorithm for real-time object tracking. It presents the following:
1) The algorithm uses kernel density estimation to calculate the similarity between a target model and candidate windows, using the Bhattacharyya coefficient. 2) It can successfully track objects moving uniformly at slow speeds but struggles with fast or non-uniform motion, or changes in scale. 3) The algorithm was tested on video streams and could track objects moving slowly but failed for fast or irregular motion. Adaptive target windows are needed to handle changes in scale.
National Flags Recognition Based on Principal Component Analysisijtsrd
Recognizing an unknown flag in a scene is challenging due to the diversity of the data and to the complexity of the identification process. And flags are associated with geographical regions, countries and nations. But flag identification of different countries is a challenging and difficult task. Recognition of an unknown flag image in a scene is challenging due to the diversity of the data and to the complexity of the identification process. The aim of the study is to propose a feature extraction based recognition system for Myanmar's national flag. Image features are acquired from the region and state of flags which are identified by using principal component analysis PCA . PCA is a statistical approach used for reducing the number of features in National flags recognition system. Soe Moe Myint | Moe Moe Myint | Aye Aye Cho "National Flags Recognition Based on Principal Component Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26775.pdfPaper URL: https://www.ijtsrd.com/other-scientific-research-area/other/26775/national-flags-recognition-based-on-principal-component-analysis/soe-moe-myint
Flower Classification Using Neural Network Based Image ProcessingIOSR Journals
Abstract: In this paper, it is proposed to have a method for classification of flowers using Artificial Neural Network (ANN) classifier. The proposed method is based on textural features such as Gray level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT). A flower image is segmented using a threshold based method. The data set has different flower images with similar appearance .The database of flower images is a mixture of images taken from World Wide Web and the images taken by us. The ANN has been trained by 50 samples to classify 5 classes of flowers and achieved classification accuracy more than 85% using GLCM features only. Keywords: Artificial Neural Network, DWT, GLCM, Segmentation.
The document summarizes research using Cosmo-SkyMed SAR images to automatically extract features and classify land cover in suburban areas. A neural network classifier achieved over 80% accuracy distinguishing four classes (asphalt, vegetation, trees, manmade structures) using backscatter intensity and GLCM texture features. Ongoing work includes optimizing the algorithm and incorporating information from multiple dates, polarizations and a change detection method.
This document contains descriptions and microscope images of several types of marine microalgae collected from Casco Bay in South Portland, Maine on March 6th and 17th, 2014. It identifies and provides details about the distinctive morphological features of several cennate and pennate diatom species, including Chaetoceros danicus, Chaetoceros debillas, Chaetoceros decipiens, Cylindrotheca closterium, Ditylum brightwellii, and Licmophora flabellata. For each type of algae, the document notes the sample source and magnification used for the accompanying microscope image.
Grey-level Co-occurence features for salt texture classificationIgor Orlov
This document summarizes a master's thesis on using grey-level co-occurrence matrices (GLCMs) to classify salt textures in seismic images. The thesis tested different GLCM parameters, developed a new "distance GLCM" feature, and evaluated Gaussian classifiers on various feature combinations. Key results included finding isotropic orientation with a 51x51 window size produced optimal GLCMs, and a classifier using contrast, distance GLCM, and weighted energy features performed best visually on test images. The thesis demonstrated GLCMs for salt texture classification but noted improvements could include 3D GLCMs, combining other texture methods, and generalizing the distance GLCM feature beyond specific class mappings.
Microalgae as a substitute for soya bean meal in the grass silage based dairy...Marjukka Lamminen
Oral presentation in the 5th EAAP International Symposium on Energy and Protein Metabolism and Nutrition (ISEP 2016), 12-15 September 2016, Krakow, Poland.
This document classifies and describes different divisions of algae. It discusses five main divisions: Chlorophyta, Euglenophyta, Pyrrophyta, Chrysophyta, and Cyanophyta. For each division, it outlines their characteristic classes, orders, pigments, food reserves, thallus structure, and modes of reproduction. Key examples are provided for representative orders within each division. The classification system serves to organize the wide diversity of algal types based on their shared morphological and physiological traits.
The document experimentally investigates conditions for producing hypochlorous acid water with high efficiency. It examines the effects of various parameters on the production efficiency, defined as the ratio of actual available chlorine produced to the theoretical maximum. Tests were conducted using a flow reactor with parallel electrode plates and no separating membrane. Results show that production efficiency is strongly affected by flow rate and current density. Higher flow rates and lower current densities yielded more efficient production, with an optimum efficiency found around 20,000 mg/L NaCl concentration. Maintaining appropriate conditions can lead to high concentration, high efficiency hypochlorous acid production.
This document summarizes an experimental study that investigated heat transfer enhancement in rectangular fin arrays with circular perforations. The researchers measured heat transfer and other parameters for solid and perforated fins under varying flow conditions. For the perforated fins, they found enhancement in heat transfer compared to solid fins. Specifically, they tested parallel and cross fins made of aluminum with dimensions of 100mm by 60mm by 5mm thickness. Testing was done with air flow velocities from 3000-6000 Reynolds number. Temperature and other measurements were taken over time as heat was applied. Calculations were done to determine heat transfer coefficients, finding values of 233.3 W/m2-K for parallel fins and 242.58 W/m2-K for cross fins
This document proposes a new approach called the Count based Secured Hash Algorithm. It introduces a new parameter β that represents the number of bits rotated to the right in the preprocessing step, which depends on the count of 1s in the input message. This increases security compared to traditional SHA algorithms where the rotation is fixed. It modifies the SHA-256, SHA-384 and SHA-512 functions by replacing the fixed rotation with a rotation based on the count. The algorithm has higher time complexity but provides better security by making the digest dependent on the message content through the count variable.
This document analyzes the effects of openings in shear walls on the seismic performance of 15-story reinforced concrete buildings. Finite element models of buildings with different shear wall configurations and opening sizes/shapes are created and analyzed using seismic coefficient and response spectrum methods. The results show that external and internal shear walls reduce column moments and axial forces compared to core-only walls. Larger openings decrease shear wall stiffness and increase seismic demands on structural elements. Response spectrum analysis predicts lower forces than the seismic coefficient method. In conclusion, properly incorporating shear walls can improve seismic performance, but openings negatively impact the walls based on their size, shape, and location.
The document analyzes vibration signals from the main gearboxes of two pumping units, labeled 1# and 2#, to diagnose any faults. It finds that the 2# gearbox's maximum vibration amplitude is about 2.5 times higher than the 1# gearbox. Narrowband filtering around the meshing frequency reveals significant differences between the envelopes, with the 2# gearbox showing a more prominent single frequency component. Oil analysis also indicates more wear in the 2# gearbox. This suggests the presence of minor faults, though vibration intensities are similar, showing modulated signals may be more sensitive for monitoring and diagnosis. The source of the prominent frequency in the 2# gearbox requires further investigation.
The document analyzes the impact of high ambient temperatures on the performance of gas turbine power plants in tropical climate zones. It presents a mathematical model to study the effect of ambient temperature on a gas turbine plant in northern Saudi Arabia. The model shows about a 20% reduction in power output when temperatures rise from the design condition of 15°C to actual summer highs of 50°C. The document then evaluates the economic justification for adding an absorption chiller to the plant to recover lost power, finding the payback period would be 1.14 years or less. It recommends gas turbines with inlet air cooling for future plants in hot climates.
This document provides an overview of smart paper technology, also known as electronic paper or e-paper. It discusses the history and development of e-paper from early technologies like Gyricon to current electrophoretic displays. Construction involves a front electronic ink layer and backplane circuitry. E-paper provides benefits over LCD like a wide viewing angle, ability to read in sunlight, and not requiring power to hold images. Applications include e-readers, watches, signs, and other portable displays.
Correlation Coefficient Based Average Textual Similarity Model for Informatio...IOSR Journals
The document presents a proposed model for a textual similarity approach for information retrieval systems in wide area networks. It evaluates the performance of four similarity functions (Jaccard, Cosine, Dice, Overlap) using correlation coefficients. Three approaches are proposed: 1) Combining Cosine and Overlap similarity scores, which performed best. 2) Combining Cosine, Dice, and Overlap scores. 3) Combining all four similarity functions. The model is represented as a triangle where the vertices are the results from the three proposed approaches to measure textual similarity between retrieved documents.
Study of Characteristics of Capacitors, Having Non-Traditional Conical Electr...IOSR Journals
This document discusses a study of the characteristics of capacitors with non-traditional conical electrode shapes, compared to traditional parallel plate capacitors. The study found that capacitors with conical electrodes exhibit non-linear exponential or polynomial relationships for capacitance, charge distribution, and current sharing along the electrodes. This is unlike parallel plate capacitors which show linear relationships. The non-uniform characteristics of conical electrode capacitors could enable applications like tuning non-linear inductive loads or use in series or shunt connections.
Development of a D.C Circuit Analysis Software Using Microsoft Visual C#.NetIOSR Journals
The document describes the development of a DC circuit analysis software called CiRSiS using Microsoft Visual C#.Net. The software can analyze purely resistive planar circuits using mesh and nodal analysis. It displays the current direction in each component as well as a current-voltage-power table for each component. The software was tested on ladder circuits with up to 4 loops and bridge circuits, showing results that correlate 99.3396% with manual calculations, making it reliable for circuit analysis and simulation.
Hardy-Steklov operator on two exponent Lorentz spaces for non-decreasing func...IOSR Journals
The document presents theorems characterizing when the Hardy-Steklov operator is bounded from one two-exponent Lorentz space to another. Specifically, it provides conditions on weights v and w such that the operator is bounded from L(0,∞)qpv to L(0,∞)srw. It defines the Hardy-Steklov operator and two-exponent Lorentz spaces. It states two theorems that characterize the weights using inequalities involving the weights and derivatives of the functions defining the Hardy-Steklov operator. The theorems assume the functions satisfy certain conditions like being strictly increasing and having derivatives satisfying an inequality.
Software Development Multi-Sourcing Relationship Management Model (Sdmrmm) P...IOSR Journals
This document describes a systematic literature review protocol to investigate challenges and critical success factors for managing relationships in software development multi-sourcing. Multi-sourcing involves one client contracting with multiple vendors. The review aims to identify challenges vendors face in establishing and maintaining relationships with clients and other vendors. It also seeks to identify critical factors for developing long-lasting relationships. The anticipated outcomes are a list of key challenges and success factors to help vendors improve multi-sourcing relationship management. The protocol outlines the planning, search strategy, and reporting that will be followed to conduct the review.
This document discusses quality of service (QoS) in Multiprotocol Label Switching (MPLS) networks. It begins with an abstract that provides an overview of MPLS and how it can improve network traffic flow and management by assigning labels to packets. The document then analyzes an MPLS network using an OPNET simulator. It explores various aspects of MPLS including its architecture, forwarding process, labels, label switching paths and how routers distinguish between labeled and unlabeled frames. The goal is to evaluate QoS performance in MPLS networks.
Human Skin Cancer Recognition and Classification by Unified Skin Texture and ...IOSR Journals
This document presents a novel method for automatically segmenting skin lesions in macroscopic images using iterative stochastic region merging based on discrete wavelet transformation. It aims to address challenges like illumination variation, presence of hair, irregular skin color variation, and multiple unhealthy skin regions. The method divides an input image into regions, extracts features like color, texture, skewness and kurtosis, then classifies the image using knowledge-based classification. Experimental results on 60 real images show the proposed method achieves lower segmentation error than level set active contours, skin lesion segmentation, and multidirectional gradient vector flow methods.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
A Review of Image Classification TechniquesIRJET Journal
This document provides a review of various image classification techniques. It begins by defining image classification as the process of assigning pixels to finite classes based on their data values. The techniques can be categorized as supervised or unsupervised. Supervised techniques use training data to define decision boundaries, while unsupervised techniques automatically partition data without labels. Common supervised techniques discussed include parallelpiped, minimum distance, and maximum likelihood classification. Unsupervised techniques include hierarchical and partitioning clustering. The document also explores hard and soft classifiers, and how combinations of techniques can improve accuracy over single methods.
This document discusses using particle swarm optimization (PSO) to design optimal close-range photogrammetry networks. PSO is introduced as a heuristic optimization algorithm inspired by bird flocking behavior that can be used to solve complex optimization problems. The document then provides an overview of close-range photogrammetry network design and the four design stages. It explains that PSO will be used to optimize the first stage of determining optimal camera station positions. Mathematical models of PSO for close-range photogrammetry network design are developed. Experimental tests are carried out to develop a PSO algorithm that can determine optimum camera positions and evaluate the accuracy of the developed network.
IRJET- Image Segmentation Techniques: A SurveyIRJET Journal
1) The document discusses various techniques for image segmentation including histogram-based techniques, K-means clustering, fuzzy C-means clustering, and watershed segmentation.
2) Histogram-based techniques use the histogram of pixel intensities or colors to separate an image into regions but do not capture much detail. K-means and fuzzy C-means are clustering techniques that group similar pixels but do not consider spatial relationships.
3) The document surveys recent research on applying these techniques and combinations such as initializing fuzzy C-means with histograms to improve convergence speed and incorporating spatial data.
A Survey on: Hyper Spectral Image Segmentation and Classification Using FODPSOrahulmonikasharma
The Spatial analysis of image sensed and captured from a satellite provides less accurate information about a remote location. Hence analyzing spectral becomes essential. Hyper spectral images are one of the remotely sensed images, they are superior to multispectral images in providing spectral information. Detection of target is one of the significant requirements in many are assuc has military, agriculture etc. This paper gives the analysis of hyper spectral image segmentation using fuzzy C-Mean (FCM)clustering technique with FODPSO classifier algorithm. The 2D adaptive log filter is proposed to denoise the sensed and captured hyper spectral image in order to remove the speckle noise.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Validation Study of Dimensionality Reduction Impact on Breast Cancer Classifi...ijcsit
A fundamental problem in machine learning is identifying the most representative subset of features from
which we can construct a predictive model for a classification task. This paper aims to present a validation
study of dimensionality reduction effect on the classification accuracy of mammographic images. The
studied dimensionality reduction methods were: locality-preserving projection (LPP), locally linear
embedding (LLE), Isometric Mapping (ISOMAP) and spectral regression (SR). We have achieved high
rates of classifications. In some combinations the classification rate was 100%. But in most of the cases the
classification rate is about 95%. It was also found that the classification rate increases with the size of the
reduced space and the optimal value of space dimension is 60. We proceeded to validate the obtained
results by measuring some validation indices such as: Xie-Beni index, Dun index and Alternative Dun
index. The measurement of these indices confirms that the optimal value of reduced space dimension is
d=60.
A Novel Approach Based on Decreased Dimension and Reduced Gray Level Range Ma...IJECEIAES
The human eye can easily identify the type of textures in flooring of the houses and in the digital images visually. In this work, the stone textures are grouped into four categories. They are bricks, marble, granite and mosaic. A novel approach is developed for decreasing the dimension of stone image and for reducing the gray level range of the image without any loss of significant feature information. This model is named as “Decreased Dimension and Reduced Gray level Range Matrix (DDRGRM)” model. The DDRGRM model consists of 3 stages. In stage 1, each 5×5 sub dimension of the stone image is reduced into 2×2 sub dimension without losing any important qualities, primitives, and any other local stuff. In stage 2, the gray level of the image is reduced from 0-255 to 0-4 by using fuzzy concepts. In stage 3, Cooccurrence Matrix (CM) features are derived from the DDRGRM model of the stone image for stone texture classification. Based on the feature set values, a user defined algorithm is developed to classify the stone texture image into one of the 4 categories i.e. Marble, Brick, Granite and Mosaic. The proposed method is tested by using the K-Nearest Neighbor Classification algorithm with the derived texture features. To prove the efficiency of the proposed method, it is tested on different stone texture image databases. The proposed method resulted in high classification rate when compared with the other existing methods.
The document presents a study that implemented segmentation and classification techniques for mammogram images to detect breast cancer malignancy. It used Gray Level Difference Method (GLDM) and Gabor texture feature extraction methods with Support Vector Machine (SVM) and K-Nearest Neighbors (K-NN) classifiers. The results showed that GLDM features with SVM achieved the best classification accuracy of 95.83%, outperforming the other combinations. The study concluded the GLDM and SVM approach provided the most effective classification of mammogram images.
IRJET- Object Detection using Hausdorff DistanceIRJET Journal
This document proposes a new object recognition system using Hausdorff distance. The system aims to improve on existing methods like YOLO that struggle with small objects and can capture garbage data. The document outlines preprocessing steps like noise cancellation, representing shapes as point sets, and extracting features. It then describes using Hausdorff distance and shape context to find the best match between input and reference shapes. Testing on datasets showed encouraging results for recognizing handwritten digits.
SYNOPSIS on Parse representation and Linear SVM.bhavinecindus
1. The document discusses a thesis on using sparse feature parameterization and multi-kernel SVM for large scale scene classification. The objective is to improve accuracy for large datasets using sparse representations and machine learning algorithms.
2. Key challenges include high dimensionality reducing accuracy for large datasets, nonlinear distributions, and computational costs of deep learning models. The research aims to address these issues.
3. The motivation from literature shows that multi-kernel SVMs have proved effective but could be improved by minimizing redundancy and optimizing kernel parameters for feature sets.
IRJET - Object Detection using Hausdorff DistanceIRJET Journal
This document proposes using Hausdorff distance for object detection as it can better handle noise compared to other methods like Euclidean distance. The document discusses preprocessing images using Gaussian filtering for noise cancellation. It then represents shapes as point sets for feature extraction before using Hausdorff distance to match shapes between reference and test images for object recognition. Encouraging results were obtained when testing on MNIST, COIL and private handwritten digit datasets.
PERFORMANCE ANALYSIS USING SINGLE SEEDED REGION GROWING ALGORITHMAM Publications
Image segmentation is an important process and its results are used in many image processing
applications. Color images can increase the quality of segmentation, but increase the complexity of the problem. This
paper focuses on measurement of parameters that is RI,GCE,MMSE and time for segmentation using "Seeded Region
growing algorithm". Image segmentation techniques using region growing requires initial seeds selection, which
increases computational cost & execution time. To overcome this problem, a single seeded region growing technique for
image segmentation is proposed, which starts from the center pixel of the image as the initial seed. It grows region
according to the grow formula and selects the next seed from connected pixel of the region. The optimization is done
with fuzzy logic to improve the value of parameters.
Medical Images are regularly of low contrast and boisterous/Noisy (absence of clarity) because of
the circumstances they are being taken. De-noising these pictures is a troublesome undertaking as they
ought to exclude any antiquities or obscuring of edges in the pictures. The Bayesian shrinkage strategy has
been chosen for thresholding in light of its sub band reliance property. The spatial space and Wavelet
based de-noising systems utilizing delicate thresholding strategy are contrasted and the proposed technique
utilizing GA (Genetic Algorithm) is used. The GA procedure is proposed in view of PSNR and results are
contrasted and existing spatial space and wavelet based de-noising separating strategies. The proposed
calculation gives improved visual clarity to diagnosing the restorative pictures. The proposed strategy in
view of GA surveys the better execution on the premise of the quantitative metric i.e PSNR (Peak Signal
to Noise-Ratio) and visual impacts. Reenactment results demonstrate that the GA based proposed
technique beats the current de-noising separating strategies.
IRJET- Image Segmentation Techniques: A ReviewIRJET Journal
1. The document discusses and reviews various techniques for image segmentation, including edge detection, threshold-based, region-based, and neural network-based methods.
2. Edge detection separates images by detecting changes in pixel intensity or color to find edges and boundaries. Threshold-based methods segment images based on pixel intensity levels compared to a threshold. Region-based methods partition images into homogeneous regions of connected pixels. Neural network-based methods can perform automated segmentation through supervised or unsupervised machine learning.
3. Prior research has evaluated these techniques, finding that edge detection works best with clear edges but struggles with noise or smooth boundaries, and thresholding methods can miss details but are simple to implement. Region-based and neural network
EDGE DETECTION IN SEGMENTED IMAGES THROUGH MEAN SHIFT ITERATIVE GRADIENT USIN...ijscmcj
In this paper, we propose a new method for edge detection in obtained images from the Mean Shift iterative algorithm. The comparable, proportional and symmetrical images are de?ned and the importance of Ring Theory is explained. A relation of equivalence among proportional images are de?ned for image groups in equivalent classes. The length of the mean shift vector is used in order to quantify the homogeneity of the neighborhoods. This gives a measure of how much uniform are the regions that compose the image. Edge detection is carried out by using the mean shift gradient based on symmetrical images. The difference among the values of gray levels are accentuated or these are decreased to enhance the interest region contours. The chosen images for the experiments were standard images and real images (cerebral hemorrhage images). The obtained results were compared with the Canny detector, and our results showed a good performance as for the edge continuity.
Texture Segmentation Based on Multifractal Dimension ijsc
This document presents a new texture segmentation algorithm based on multifractal dimension. The algorithm divides an image into blocks and extracts feature vectors for each block using box counting method on multiple thresholds of the image. A supervised learning phase is used to classify blocks based on these feature vectors by extracting mean and standard deviation values for sample windows labeled by an expert. The algorithm was tested on multi-texture images by extracting feature vectors for each small block and classifying them based on the trained classifier.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Similar to A Review of Recent Texture Classification: Methods (20)
This document provides a technical review of secure banking using RSA and AES encryption methodologies. It discusses how RSA and AES are commonly used encryption standards for secure data transmission between ATMs and bank servers. The document first provides background on ATM security measures and risks of attacks. It then reviews related work analyzing encryption techniques. The document proposes using a one-time password in addition to a PIN for ATM authentication. It concludes that implementing encryption standards like RSA and AES can make transactions more secure and build trust in online banking.
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This document provides a review of mobility management techniques in vehicular ad hoc networks (VANETs). It discusses three modes of communication in VANETs: vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), and hybrid vehicle (HV) communication. For each communication mode, different mobility management schemes are required due to their unique characteristics. The document also discusses mobility management challenges in VANETs and outlines some open research issues in improving mobility management for seamless communication in these dynamic networks.
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Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
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- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
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geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
A Review of Recent Texture Classification: Methods
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 14, Issue 1 (Sep. - Oct. 2013), PP 54-60
www.iosrjournals.org
www.iosrjournals.org 54 | Page
A Review of Recent Texture Classification: Methods.
M. Venkataramana , Asst.prof ,Prof .E.Sreenivasa Reddy ,
Prof CH.Satyanarayana ,S.Anuradha
1 Asst.Prof, Dept Of CSE, Gitam University, Visakapatnama, India.2
Prof, Dept Of CSE Nagarjunauniversity,
3
Prof.Dept Of CSE Jntu Kakinada, India.4.Asst.Prof CSE Dept Gitam University.
Abstract: Texture classification is used in various pattern recognition applications that possess feature-liked
Appearance. This paper aims to compile the recent trends on the usage of feature extraction and classification
methods used in the research of texture classification as well as the texture datasets used for the experiments.
The study shows that the signal processing methods, such as Gabor filters and wavelets are gaining popularity
but old methods such as GLCM are still used but are improved with new calculations or combined with other
methods. For the classifiers, nearest neighbor algorithms are still fairly popular despite being simple and SVM
has become a major classifier used in texture classification. For the datasets, DynTex, Brodatz texture dataset is
the most popularly used dataset despite it being old and with limited samples, other datasets are less used.
Index Terms: Texture Classification, wavelet –Based Dynamic, Computer Vision, Pattern Recognition,
Machine Learning.
I. Introduction
Texture classification is the process to classify different textures from the given images. Although the
classification of textures itself often seems to be meaningless in its own sense, texture classification can
however be implemented a large variety of real world problems involving specific textures of different objects
[1]. Some of the real world applications that involve textured objects of surfaces include rock classification [2],
wood species recognition [3], face Detection [4], fabric classification [5], geographical landscape segmentation
[6] and etc. All these applications allowed the target subjects to be viewed as a specific type of texture and
hence they can be solved using texture classification techniques. Texture classification techniques are grouped
up in five main groups in general, namely 1) structural; 2) statistical; 3) signal processing; 4) model-based
stochastic [1], and; 5) morphology-based methods [7]. Out of the five groups, statistical and signal processing
methods are the most widely used because they can be directly applied onto any type of texture. The rest are not
as widely used because the structural methods need to implemented on structured textures which are naturally
rare, the model based stochastic methods are not easily implemented due to the complexity to estimate the
parameters and morphology-based methods are relatively new and the process are very simple, they may not
promise very good textural features.
The main objective of this paper is to compile the recent trends in texture classification in terms of
feature extraction and classification methods used as well as the texture datasets used in the training and testing
process within the last five years. Section 2 shows the feature extraction methods used in the recent years.
Section 3 shows the classification methods used in the recent years. Section 4 shows the popularly
II. Feature Extraction Methods
There are many different feature extraction methods that were introduced and used for texture
classification problems. Most of these methods that were popularly used in recent years were statistical and
signal processing methods.
2.1. GLCM
Grey Level Co-occurrence Matrices (GLCM) is an old feature extraction for texture classification that
was proposed by Haralick et al. back in 1973 [8]. It has been widely used on many texture classification
applications and remained to be an important feature extraction method in the domain of texture classification. It
is a statistical method that computes the relationship between pixel pairs in the image. In the conventional
method, textural features will be calculated from the generated GLCMs, e.g. contrast, correlation, energy,
entropy and homogeneity [9]. However in recent years, the GLCM is often combined with other methods and is
rarely used individually [6, 10, and 12]. Other than the conventional implementation, there are a few other
implementations of the GLCM, e.g. by introducing a second-order statistical method on top of the textural
features in the original implementation [12], one-dimensional GLCM [13] and using the raw GLCM itself
instead of the first-order statistics [14]. The GLCM can also be applied on different color space for color co
occurrence matrix [15].
2. A Review of Recent Texture Classification: Methods.
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2.4. Wavelets Methods
Wavelet transforms is another signal processing method that have been implemented in image
processing and pattern recognition for the last two decades [26]. It is currently an important feature to be used in
texture classification and has been very popularly used [12, 27-42]. The Discrete Wavelet Transforms (DWT) is
among the most popularly used wavelet transforms, some basic discrete wavelets include the Haar wavelet and
Daubechies wavelets. Like the Gabor filters, the wavelet transform are preformed on the frequency domain of
the images rather than the spatial domain. The information on the frequency domain is usually more stable than
the spatial domain. Therefore, they often produces better features that leads to a higher accuracy despite being
more complex and slower. Some other transforms that were used includes the curvelet transform [10, 18, 47-49]
and the Wavelets based Dynamic Texture classification using Gumble Distribution [11] as well as a few other
transforms that were less popularly used. E.g. ridge let transform [50, 51, 52], log polar transform [52], Radon
transform [53] and etc.
2.5. Independent Component Analysis (ICA)
PCA had been used for feature extraction, but it has the limitation of only obtaining up to second-order
statistics but ICA can overcome this problem and is capable of obtaining higher order statistics. [54]. It is used
to separate a multivariate signal which is also implemented in texture classification but is not very popular [54-
57].
2.7. Other Feature Extractions
There are many other feature extractions that are not popularly used in recent years which some are
recently proposed, including model-based stochastic methods, e.g. fractals [59, 60] and Markov random field
[61, 62]. Also includes some other methods, e.g. Sequential Approximation Error Curves (SAEC) [63], Basic
Image Features [64], Spectral Correlation Function (SCF) [65], Legendre Spectrum [66] and Multiscale Blob
Features (MBF) [67].
2.8. Summary and Findings on Feature Extractions
It is easily noticeable that signal processing methods are very popularly used in the recent years,
especially for Gabor filters and wavelets. Although these methods require more computation as they are
examining the frequency domain, the accuracy obtained is good and usually outperform older and simpler
techniques. The old technique like GLCM is however yet to be forgotten in the field of texture classification
because it is one of the simplest textural feature which is old but is computationally inexpensive. It remains to
be mainly used as a baseline algorithm for comparative studies especially when a new application of texture
classification is experimented [5]. The GLCM is however more commonly used in some improved or combined
ways recently but none of these variants have grown into a major trend. The major trend of the research today in
terms of feature extraction for texture classification is accuracy oriented, however usually the newer algorithms
that promises better accuracy is much more complicated in its calculations and often sacrifices the speed of the
algorithm. The signal processing methods for example is a relatively slow algorithm with a higher accuracy
[14]. Dynamic texture classification has attracted growing attention. Characterization of dynamic texture is vital
to address the classification problem. The region covariance matrix is new in the area of texture classification. It
has the potential to become the next trend due to its fast computations using integral images
III. Classification Methods
There are three major groups of classifiers are popularly used, including nearest neighbors, Artificial
Neural Networks (ANN) and Support Vector Machines (SVM). Besides them, there are also other less popularly
used classifiers or classification algorithms
3.1. Nearest Neighbors
The nearest neighbor algorithms are simple classifiers that select the training samples with the closest
distance to the query sample. These classifiers will compute the distance from the query sample to every
training sample and select the best neighbor or neighbors with the shortest distance. The k-Nearest Neighbor (k-
NN) is a popular implementation where k number of best neighbors is selected and the winning class will be
decided based on the best number of votes among the k neighbors [68]. The nearest neighbor is simple to be
implemented as it does not require a training process. It is useful especially when there is a small dataset
available which is not effectively trained using other machine learning methods that goes to the training process.
However, the major drawback of the nearest neighbor algorithms is that the speed of computing distance will
increase according to the number of training samples available[77].
3. A Review of Recent Texture Classification: Methods.
www.iosrjournals.org 56 | Page
3.2. ANN
ANNs are popular machine learning algorithms that were popular for the last decade and remains to be
widely used until recent years. The basic form of ANN is the Multilayer Perception (MLP) which is a neural
network that updates the weights through back-propagation during the training. It has proven to be useful in the
past but is slowly losing popularity and is showing a trend of being taken over by the SVM that will be
discussed in Section 3.3. Other variants of neural networks were also implemented in texture classification
recently such as the Probabilistic Neural Network (PNN) [22, 69]. The Convolution Neural Network (CoNN) is
a neural network that has convolution input layers that acts as a self learning feature extractor directly from the
raw pixels of the input images. Therefore, it can perform both feature extraction and classification under the
same architecture [70].
3.3. SVM
SVM are the newer trends in machine learning algorithm which is popular in many pattern recognition
problems in recent years, including texture classification. SVM is designed to maximize the marginal distance
between classes with decision boundaries drawn using different kernels [41]. SVM is designed to work with
only two classes by determining the hyper plane to ivied two classes. This is done by maximizing the margin
from the hyper plane to the two classes. The samples closest to the margin that were selected to determine the
hyper plane is known as support vectors. Multiclass classification is also applicable, the multiclass SVM is
basically built up by various two class SVMs to solve the problem, either by using one-versus-all or one-versus-
one. The winning class is then determined by the highest output function or the maximum votes respectively.
This may cause the multiclass SVM to perform slower than the MLPs. Despite that, SVM is still considered to
be powerful classifier which was replacing the ANN and has slowly evolved into one of the most important
main stream classifier. They are now widely used in the research of texture classification.
3.4. Other Classifiers
Other classifiers are also used for texture classification but has yet to be popular in the recent years, e,
g. the Bayes classifier [59, 71], Learning Vector Quantization (LVQ)[47] and Hidden Markov Model (HMM)
[72].
3.5. Summary and Findings on Classification Methods
SVM is today not only the major trend in texture classification, it is also generally a very popular
classifier in various pattern recognition problems, including recognition and detection problems. However it was
not initially designed for multiclass problems, therefore it is adapted to implement of multiclass problem which
is more complicated and will be slightly slower. As a comparison, the SVM has the best accuracy performance
compared to ANN and nearest neighbors. But SVM and nearest neighbors are both required to store the sample
points that helps to classify the problem space. In the nearest neighbors, all sample points have to be stored but
in SVM, only the chosen samples which are of good representation to classify the problem space will be stored
and known as support vectors. The ANN model however only requires the weights in the ANN model to be tune
to represent the problem space, hence its needs in storing information is usually less than SVM and nearest
neighbors. ANN and Bayes classifiers are often a classifier to be chosen due to their fast speed performance
because their classification stages involved simpler calculations which helps to produce fast output results. The
trend of the classifiers did not evolve specially for texture classification as it is generally following the trend of
general machine learning. This showed that the texture classification does not require specific classifiers as
compared to feature extractors. It is likely that the trend of usage for classifiers will continue to follow the major
trends of machine learning.
IV. Texture Datasets
There are a number of texture datasets that were used in experiments on texture classification, e.g. the
Brodatz texture album and CUReT texture dataset which were more widely used.
4.1. Brodatz Texture Album
The Brodatz textures are popular and widely used as benchmark datasets in texture classification. It is
consists of 112 textures that were abstracted from the Brodatz texture album [73]. Each of these textures is
produced from a single image scanned from the texture album.
Although the Brodatz textures are widely used, there are many different subsets of the dataset which
often involves only part of the album and some with rotated and scaled samples added [74]. The entire dataset
are sometimes also used [75]. Sample images of the Brodatz textures are Shown in Figure 4.1.
4. A Review of Recent Texture Classification: Methods.
www.iosrjournals.org 57 | Page
Fig 4.1. Samples of four images from the Brodatz texture album.
The main limitation of the Brodatz texture dataset is that all the textures are represented by a single image only.
Therefore, the users need to segment to dataset into smaller segments, and often scale and rotate them. Since
there is only a single image for each texture, the segments of the same texture will be rather homogeneous for
both training and testing
.
4.2. CUReT Dataset
The Columbia-Utrecht Reflectance and Texture (CUReT) dataset is produced in a collaborated research
between Columbia University and Utrecht University [76]. The dataset includes 61 different textures with 92
images for each class. These images are acquired under different illuminations and viewing directions [77].
With the differences in illuminations and viewing directions, this dataset creates greater challenge to the
algorithm which should tackle the problem on illumination and direction since both of these factors could cause
the samples to be showing different appearance, unlike the Brodatz dataset that only provide one single
illumination and viewing direction for each texture. Sample images of the CUReT textures are shown in Figure
4.2.
Fig 4.2. Samples of images from the CUReT Texture album.
4.3. VisTex Dataset
The Vision Texture (VisTex) dataset is prepared by the Massachusetts Institute of Technology (MIT)
[78]. The dataset is not only consisting of homogeneous frontal acquisition of textures, it also comes with real-
world scenes with multiple textures and video textures. This dataset is not very popularly used but more
frequent than those in Section 4.4. Sample images of the dataset are shown in Figure 4.3.
Fig 4.3. Samples of four images from the VisTex Dataset textures
4.4. DynTex Dataset
The DynTex database is a diverse collection of high-quality dynamic texture videos. Currently we are
finalizing the structure of the database.
Dynamic textures are typically result from processes such as of waves, smoke, fire, a flag blowing in
the wind, a moving escalator, or a walking crowd. Many real-world textures occurring in video databases are
dynamic and retrieval should be based on both their dynamic and static features. Important tasks are thus the
detection, segmentation and perceptual characterization of dynamic textures. Sample images of the dataset are
shown in Figure 4.4.
Fig 4.4. Samples of four images from the DynTex textures
5. A Review of Recent Texture Classification: Methods.
www.iosrjournals.org 58 | Page
4.5. Other Datasets
There are a few other datasets that were less popularly used, e.g. the MeasTex [19], PhoTex [32],
OuTex [19, 47], KTH-TIPS [64, 77] and UIUCTex [60, 64, and 77].
4.5. Summary and Findings on Texture Datasets
The common problem of texture classification today is that texture datasets that are available in the
field are usually having each textures acquired only once, therefore each class often have very homogeneous
training and testing samples. This problem has not been solved due to the difficulty in preparing datasets with
different acquisition of a same type of texture for all the textures. The Brodatz texture album is a printed album
that is available backed in 1996. The scanned textures are then popularly used in the research of texture
classification as a popular benchmark dataset. Other newer datasets have yet to be very popularly used but has
often included more aspect, such as variations in viewing angle and illumination. Some of this dataset, e.g. the
VisTex, DynTexis still being improved as video textures are planned to be included in the future [78]. As the
Brodatz dataset has currently been used as a dominant benchmark dataset, in the future, the other datasets are
likely to gain popularity as the trend of research will likely to move from accuracy-oriented towards tackling the
issue on viewing angles and illuminations. Datasets with multiple instants of the same texture acquired from
different materials will also be useful for future research in handling the variances within the textures itself.
V. Conclusion
In this paper, the trend of usage in the feature extraction methods, classification methods and texture
datasets in the last five years is discovered. For the feature extraction methods, wavelet transforms and other
signal processing methods are among the most popularly used feature extraction due to their promising
accuracy. Surprisingly, old methods such as GLCM are still used but their implementations are improved or
combined with other methods. In terms of the classification method, SVM has took over ANN as the most
commonly used classifier which has also proven to be able to outperform the ANN in terms of accuracy. For the
experimental datasets, variants of the Brodatz texture datasets remained to be the most popular benchmark
dataset in the research of texture classification. The trend of the usage for the feature extraction and
classification method showed that the researches are mainly accuracy-oriented, where the signal processing
methods and SVM could produce a better accuracy but these methods are often more complex than older
methods and could not guarantee a better speed performance. The increase in computational capabilities of the
computers today has assisted in the growth of the research by allowing more complex algorithms to work within
a reasonable time. However, when a computer with lower computational power is being concerned, e.g. an
embedded platform, an older yet simpler method is often more useful [14]. As smart phones and other compact
devices are gaining popularity, texture classification-based applications that can be run on these platforms will
require higher efficiency in terms of speed while at the same time offering satisfying accuracy. Although texture
classification has been studied for decades, it has yet to come to an end as newer studies has revealed that there
are simply much more space for the research to carry on especially since it is generally useful to solve many
different real world problems. These aspects that could be focused on in future research include the speed and
required storage as well as the varying acquisition conditions. With the research on improving the accuracy
being carried out for years and is slowly moving against its bottleneck, the perspective on solving illumination
and viewing angle problem, searching for a balance between speed and accuracy are likely to become more
important to be studied in the years to come.
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