The paper presents an extension of content based image retrieval (CBIR) techniques based on multilevel Block Truncation Coding (BTC) using nine sundry color spaces. Block truncation coding based features is one of the CBIR methods proposed using color features of image. The approach basically considers red, green and blue planes of an image to compute feature vector. This BTC based CBIR can be extended as multileveled BTC for performance improvement in image retrieval. The paper extends the multileveled BTC using RGB color space to other nine color spaces. The CBIR techniques like BTC Level-1, BTC Level-2, BTC Level-3 and BTC Level-4 are applied using various color spaces to analyze and compare their performances. The CBIR techniques are tested on generic image database of 1000 images spread across 11 categories. For each CBIR technique, 55 queries (5 per category) are fired on extended Wang generic image database to compute average precision and recall for all queries. The results have shown the performance improvement (ie., higher precision and recall values) with BTC-CBIR methods using luminance-chrominance color spaces (YCgCb, Kekre’s LUV, YUV, YIQ, YCbCr) as compared to non-luminance (RGB, HSI, HSV, rgb , XYZ) Color spaces. The performance of multileveled BTC-CBIR increases gradually with increase in level up to certain extent (Level 3) and then increases slightly due to voids being created at higher levels. In all levels of BTC Kekre’s LUV color space gives best performance
This document summarizes a research paper that proposes an image retrieval and re-ranking system using both text and visual queries. The system first retrieves images from the web based on a textual query submitted by the user. The user can then select multiple example images from the results to better convey their intent. The system calculates visual similarities between the example images and results based on MPEG-7 descriptors like color and texture. Distances are combined to re-rank the initial text-based search results, aiming to improve relevance by incorporating the visual query. The system is evaluated on queries like "apples", "Paris" and "Console" and shows better results than text-only searches according to the document.
Information search using text and image queryeSAT Journals
Abstract An image retrieval and re-ranking system utilizing a visual re-ranking framework which is proposed in this paper the system retrieves a dataset from the World Wide Web based on textual query submitted by the user. These results are kept as data set for information retrieval. This dataset is then re-ranked using a visual query (multiple images selected by user from the dataset) which conveys user’s intention semantically. Visual descriptors (MPEG-7) which describe image with respect to low-level feature like color, texture, etc are used for calculating distances. These distances are a measure of similarity between query images and members of the dataset. Our proposed system has been assessed on different types of queries such as apples, Console, Paris, etc. It shows significant improvement on initial text-based search results.This system is well suitable for online shopping application. Index Terms: MPEG-7, Color Layout Descriptor (CLD), Edge Histogram Descriptor (EHD), image retrieval and re-ranking system
Content-based image retrieval (CBIR) uses visual image content to search large image databases according to user needs. CBIR systems represent images by extracting features related to color, shape, texture, and spatial layout. Features are extracted from regions of the image and compared to features of images in the database to find the most similar matches. CBIR has applications in medical imaging, fingerprints, photo collections, and more. Techniques include representing images with histograms of color and texture features extracted through transforms.
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.
In this project, we proposed a Content Based Image Retrieval (CBIR) system which is used to retrieve a
relevant image from an outsized database. Textile images showed the way for the development of CBIR. It
establishes the efficient combination of color, shape and texture features. Here the textile image is given as
dataset. The images in database are loaded. The resultant image is given as input to feature extraction
technique which is transformation of input image into a set of features such as color, texture and shape.
The texture feature of an image is taken out by using Gray level co-occurrence matrix (GLCM). The color
feature of an image is obtained by HSI color space. The shape feature of an image is extorted by sobel
technique. These algorithms are used to calculate the similarity between extracted features. These features
are combined effectively so that the retrieval accuracy and recall rate is enhanced. The classification
techniques such as Support Vector Machine (SVM) are used to classify the features of a query image by
splitting the group such as color, shape and texture. Finally, the relevant images are retrieved from a large
database and hence the efficiency of an image is plotted.The software used is MATLAB 7.10 (matrix
laboratory) which is built software applications
Ijaems apr-2016-16 Active Learning Method for Interactive Image RetrievalINFOGAIN PUBLICATION
With many possible multimedia applications, content-based image retrieval (CBIR) has recently gained more interest for image management and web search. CBIR is a technique that utilizes the visual content of an image, to search for similar images in large-scale image databases, according to a user’s concern. In image retrieval algorithms, retrieval is according to feature similarities with respect to the query, ignoring the similarities among images in database. To use the feature similarities information, this paper presents the k-means clustering algorithm to image retrieval system. This clustering algorithm optimizes the relevance results by firstly clustering the similar images in the database. In this paper, we are also implementing wavelet transform which demonstrates significant rough and precise filtering. We also apply the Euclidean distance metric and input a query image based on similarity features of which we can retrieve the output images. The results show that the proposed approach can greatly improve the efficiency and performances of image retrieval.
Analysis of combined approaches of CBIR systems by clustering at varying prec...IJECEIAES
The image retrieving system is used to retrieve images from the image database. Two types of Image retrieval techniques are commonly used: content-based and text-based techniques. One of the well-known image retrieval techniques that extract the images in an unsupervised way, known as the cluster-based image retrieval technique. In this cluster-based image retrieval, all visual features of an image are combined to find a better retrieval rate and precisions. The objectives of the study were to develop a new model by combining the three traits i.e., color, shape, and texture of an image. The color-shape and colortexture models were compared to a threshold value with various precision levels. A union was formed of a newly developed model with a color-shape, and color-texture model to find the retrieval rate in terms of precisions of the image retrieval system. The results were experimented on on the COREL standard database and it was found that the union of three models gives better results than the image retrieval from the individual models. The newly developed model and the union of the given models also gives better results than the existing system named clusterbased retrieval of images by unsupervised learning (CLUE).
This document summarizes and reviews several techniques for image mining, including feature extraction, image clustering, and object recognition algorithms. It discusses color, texture, and edge feature extraction techniques and evaluates their precision and recall. It also describes the block truncation algorithm for image recognition and the cascade feature extraction approach. The key techniques - color moments, block truncation coding, and cascade classifiers - are evaluated based on experimental recall and precision results. Overall, the document provides an overview of different image mining techniques and evaluates their effectiveness.
This document summarizes a research paper that proposes an image retrieval and re-ranking system using both text and visual queries. The system first retrieves images from the web based on a textual query submitted by the user. The user can then select multiple example images from the results to better convey their intent. The system calculates visual similarities between the example images and results based on MPEG-7 descriptors like color and texture. Distances are combined to re-rank the initial text-based search results, aiming to improve relevance by incorporating the visual query. The system is evaluated on queries like "apples", "Paris" and "Console" and shows better results than text-only searches according to the document.
Information search using text and image queryeSAT Journals
Abstract An image retrieval and re-ranking system utilizing a visual re-ranking framework which is proposed in this paper the system retrieves a dataset from the World Wide Web based on textual query submitted by the user. These results are kept as data set for information retrieval. This dataset is then re-ranked using a visual query (multiple images selected by user from the dataset) which conveys user’s intention semantically. Visual descriptors (MPEG-7) which describe image with respect to low-level feature like color, texture, etc are used for calculating distances. These distances are a measure of similarity between query images and members of the dataset. Our proposed system has been assessed on different types of queries such as apples, Console, Paris, etc. It shows significant improvement on initial text-based search results.This system is well suitable for online shopping application. Index Terms: MPEG-7, Color Layout Descriptor (CLD), Edge Histogram Descriptor (EHD), image retrieval and re-ranking system
Content-based image retrieval (CBIR) uses visual image content to search large image databases according to user needs. CBIR systems represent images by extracting features related to color, shape, texture, and spatial layout. Features are extracted from regions of the image and compared to features of images in the database to find the most similar matches. CBIR has applications in medical imaging, fingerprints, photo collections, and more. Techniques include representing images with histograms of color and texture features extracted through transforms.
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.
In this project, we proposed a Content Based Image Retrieval (CBIR) system which is used to retrieve a
relevant image from an outsized database. Textile images showed the way for the development of CBIR. It
establishes the efficient combination of color, shape and texture features. Here the textile image is given as
dataset. The images in database are loaded. The resultant image is given as input to feature extraction
technique which is transformation of input image into a set of features such as color, texture and shape.
The texture feature of an image is taken out by using Gray level co-occurrence matrix (GLCM). The color
feature of an image is obtained by HSI color space. The shape feature of an image is extorted by sobel
technique. These algorithms are used to calculate the similarity between extracted features. These features
are combined effectively so that the retrieval accuracy and recall rate is enhanced. The classification
techniques such as Support Vector Machine (SVM) are used to classify the features of a query image by
splitting the group such as color, shape and texture. Finally, the relevant images are retrieved from a large
database and hence the efficiency of an image is plotted.The software used is MATLAB 7.10 (matrix
laboratory) which is built software applications
Ijaems apr-2016-16 Active Learning Method for Interactive Image RetrievalINFOGAIN PUBLICATION
With many possible multimedia applications, content-based image retrieval (CBIR) has recently gained more interest for image management and web search. CBIR is a technique that utilizes the visual content of an image, to search for similar images in large-scale image databases, according to a user’s concern. In image retrieval algorithms, retrieval is according to feature similarities with respect to the query, ignoring the similarities among images in database. To use the feature similarities information, this paper presents the k-means clustering algorithm to image retrieval system. This clustering algorithm optimizes the relevance results by firstly clustering the similar images in the database. In this paper, we are also implementing wavelet transform which demonstrates significant rough and precise filtering. We also apply the Euclidean distance metric and input a query image based on similarity features of which we can retrieve the output images. The results show that the proposed approach can greatly improve the efficiency and performances of image retrieval.
Analysis of combined approaches of CBIR systems by clustering at varying prec...IJECEIAES
The image retrieving system is used to retrieve images from the image database. Two types of Image retrieval techniques are commonly used: content-based and text-based techniques. One of the well-known image retrieval techniques that extract the images in an unsupervised way, known as the cluster-based image retrieval technique. In this cluster-based image retrieval, all visual features of an image are combined to find a better retrieval rate and precisions. The objectives of the study were to develop a new model by combining the three traits i.e., color, shape, and texture of an image. The color-shape and colortexture models were compared to a threshold value with various precision levels. A union was formed of a newly developed model with a color-shape, and color-texture model to find the retrieval rate in terms of precisions of the image retrieval system. The results were experimented on on the COREL standard database and it was found that the union of three models gives better results than the image retrieval from the individual models. The newly developed model and the union of the given models also gives better results than the existing system named clusterbased retrieval of images by unsupervised learning (CLUE).
This document summarizes and reviews several techniques for image mining, including feature extraction, image clustering, and object recognition algorithms. It discusses color, texture, and edge feature extraction techniques and evaluates their precision and recall. It also describes the block truncation algorithm for image recognition and the cascade feature extraction approach. The key techniques - color moments, block truncation coding, and cascade classifiers - are evaluated based on experimental recall and precision results. Overall, the document provides an overview of different image mining techniques and evaluates their effectiveness.
The content based image retrieval (CBIR) technique
is one of the most popular and evolving research areas of the
digital image processing. The goal of CBIR is to extract visual
content like colour, texture or shape, of an image automatically.
This paper proposes an image retrieval method that uses colour
and texture for feature extraction. This system uses the query by
example model. The system allows user to choose the feature on
the basis of which retrieval will take place. For the retrieval
based on colour feature, RGB and HSV models are taken into
consideration. Whereas for texture the GLCM is used for
extracting the textural features which then goes into Vector
Quantization phase to speed up the retrieval process.
This document summarizes image indexing and its features. It discusses that image indexing is used to retrieve similar images from a database based on extracted features like color, shape, and texture. Color features can be represented by models like RGB, HSV, and color histograms. Shape features include global properties like roundness and local features like edge segments. Texture is described using statistical, structural, and spectral approaches. Texture feature extraction methods discussed include standard wavelets, Gabor wavelets, and extracting features like entropy and standard deviation. The paper provides an overview of the different features used for image indexing and classification.
A Hybrid Approach for Content Based Image Retrieval SystemIOSR Journals
This document describes a hybrid approach for content-based image retrieval. It combines several spatial features - row sum, column sum, forward and backward diagonal sums - and histograms to represent images with feature vectors. Euclidean distance is used to calculate similarity between a query image's feature vector and those in the database. The approach is evaluated using precision-recall calculations on different image groups, showing the hybrid method performs best by combining multiple features.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
Automatic Image Annotation Using CMRM with Scene InformationTELKOMNIKA JOURNAL
Searching of digital images in a disorganized image collection is a challenging problem.
One step of image searching is automatic image annotation. Automatic image annotation refers
to the process of automatically assigning relevant text keywords to any given image, reflecting
its content. In the past decade many automatic image annotation methods have been proposed
and achieved promising result. However, annotation prediction from the methods is still far from
accurate. To tackle this problem, in this paper we propose an automatic annotation method
using relevance model and scene information. CMRM is one of automatic image annotation
method based on relevance model approach. CMRM method assumes that regions in an image
can be described using a small vocabulary of blobs. Blobs are generated from segmentation,
feature extraction, and clustering. Given a training set of images with annotations, this method
predicts the probability of generating a word given the blobs in an image. To improve annotation
prediction accuracy of CMRM, in this paper we utilize scene information incorporate with
CMRM. Our proposed method is called scene-CMRM. Global image region can be represented
by features which indicate type of scene shown in the image. Thus, annotation prediction of
CMRM could be more accurate based on that scene type. Our experiments showed that, the
methods provides prediction with better precision than CMRM does, where precision represents
the percentage of words that is correctly predicted.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Evaluation of Euclidean and Manhanttan Metrics In Content Based Image Retriev...IJERA Editor
This document evaluates the performance of the Euclidean and Manhattan distance metrics in a content-based image retrieval system. It finds that the Manhattan distance metric showed better precision than the Euclidean distance metric. The system uses color histograms and Gabor texture features to represent images. Color is represented in HSV color space and histograms of hue, saturation and value are used. Gabor filters are applied to capture texture at different scales and orientations. Distance between feature vectors is calculated using Euclidean and Manhattan distance formulas to find similar images from the database. The system was tested on a dataset of 1000 Corel images and Manhattan distance produced more relevant search results.
Color and texture based image retrievaleSAT Journals
Abstract Content-based image retrieval (CBIR) is an vital research area for manipulating bulky image databases and records. Alongside the conventional method where the images are searched on the basis of words, CBIR system uses visual contents to retrieve the images. In content based image retrieval systems texture and color features have been the primal descriptors. We use HSV color information and mean of the image as texture information. The performance of proposed scheme is calculated on the basis of precision, recall and accuracy. As an effect, the blend of color and texture features of the image provides strong feature set for image retrieval. Keywords: image retrieval, HSV color space, color histogram, image texture.
The document presents a method for detecting copy-move forgery in digital images using center-symmetric local binary pattern (CS-LBP). The key steps are:
1. The input image is converted to grayscale and divided into overlapping blocks.
2. CS-LBP features are extracted from each block to represent textures while being invariant to illumination and rotation. This reduces the feature dimensions compared to local binary pattern (LBP).
3. The distances between feature vectors are calculated and sorted lexicographically to group similar blocks together. Distances below a threshold indicate copied regions.
4. Post-processing with morphological operations fills holes and removes outliers to generate a mask of the forged regions.
The
Content based image retrieval using features extracted from halftoning-based ...LogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Content-Based Image Retrieval (CBIR) systems have been used for the searching of relevant images in various research areas. In CBIR systems features such as shape, texture and color are used. The extraction of features is the main step on which the retrieval results depend. Color features in CBIR are used as in the color histogram, color moments, conventional color correlogram and color histogram. Color space selection is used to represent the information of color of the pixels of the query image. The shape is the basic characteristic of segmented regions of an image. Different methods are introduced for better retrieval using different shape representation techniques; earlier the global shape representations were used but with time moved towards local shape representations. The local shape is more related to the expressing of result instead of the method. Local shape features may be derived from the texture properties and the color derivatives. Texture features have been used for images of documents, segmentation-based recognition,and satellite images. Texture features are used in different CBIR systems along with color, shape, geometrical structure and sift features.
The document presents a method for human action recognition using both RGB and depth data from an RGB-D sensor. Motion History Images (MHIs) are generated from RGB videos and Depth Motion Maps (DMMs) are generated from depth data after rotating the 3D point clouds. A 4-channel Deep Convolutional Neural Network is trained with one channel for MHIs and three channels for the rotated DMM views. Evaluated on the UTD-MHAD dataset, the proposed method achieves better recognition accuracy when fusing both RGB and depth modalities compared to using each individually.
- Content-based image retrieval (CBIR) searches for images based on visual features like color, texture, and shape rather than keywords.
- CBIR systems extract features from images to create metadata and use those features to calculate visual similarity between images.
- Relevance feedback allows users to provide feedback on initial search results to help the system recalculate feature weights and improve subsequent results.
The document discusses content-based image retrieval (CBIR) systems. It describes how CBIR systems use feature extraction to search large image databases based on visual content. The key components of CBIR systems are feature extraction, indexing, and system design. Feature extraction involves extracting information about images' colors, textures, shapes, and spatial locations. Effective features and indexing techniques are needed to make CBIR scalable for large image collections. Performance is evaluated based on how well systems return relevant images.
The students can learn about basics of image processing using matlab.
It explains the image operations with the help of examples and Matlab codes.
Students can fine sample images and .m code from the link given in slides.
This document summarizes a research paper that proposes a content-based image retrieval system using cascaded color and texture features. Color features are first extracted from images using statistical measures like mean, standard deviation, energy, entropy, skewness and kurtosis. Similarity to a query image is then measured using distance metrics. The top 150 most similar images are then analyzed to extract Haralick texture features. Similarity is again measured to retrieve the most relevant images. The paper finds that Canberra distance provides better retrieval results than other distance metrics like City Block and Minkowski.
Multi Resolution features of Content Based Image RetrievalIDES Editor
Many content based retrieval systems have been
proposed to manage and retrieve images on the basis of their
content. In this paper we proposed Color Histogram, Discrete
Wavelet Transform and Complex Wavelet Transform
techniques for efficient image retrieval from huge database.
Color Histogram technique is based on exact matching of
histogram of query image and database. Discrete Wavelet
transform technique retrieves images based on computation
of wavelet coefficients of subbands. Complex Wavelet
Transform technique includes computation of real and
imaginary part to extract the details from texture. The
proposed method is tested on COREL1000 database and
retrieval results have demonstrated a significant improvement
in precision and recall.
This document discusses various approaches to image indexing and retrieval, including using text descriptions, extracting color, shape and texture features, compressed image data, and spatial relationships. It describes common techniques like color histograms, shape representations, texture features, and using DCT, wavelet or VQ compression. An integrated approach is recommended to support both textual queries and pictorial similarity comparisons.
Literature Review on Content Based Image RetrievalUpekha Vandebona
This document summarizes a literature review on content-based image retrieval (CBIR). It discusses how CBIR uses computer vision techniques to automatically extract visual features from images for retrieval, unlike traditional concept-based methods that rely on metadata/text. The key visual features discussed are color, texture, and shape. A typical CBIR system architecture includes creating an image database, automatically extracting features, searching by example or semantics, and ranking results. Distance measures are used to compare image features and evaluate retrieval performance. Combining CBIR with concept-based techniques could improve image retrieval overall.
Empirical Coding for Curvature Based Linear Representation in Image Retrieval...iosrjce
The document presents a new approach called Linear Curvature Empirical Coding (LCEC) for image retrieval. LCEC aims to improve upon existing curvature-based coding approaches by linearly representing the curvature scale space plot and then applying empirical coding to select descriptive shape features. The linear representation considers variations across all smoothing factors rather than discarding information below a threshold. Empirical coding is used to select features based on variation density rather than just magnitude. The results show LCEC performs better than previous methods for image retrieval.
Feature Extraction in Content based Image Retrievalijcnes
This document discusses feature extraction methods for content-based image retrieval (CBIR). It describes Order Dither Block Truncation Coding (ODBTC), an image compression technique that can be used to extract image features without decoding. The proposed CBIR system extracts two features from ODBTC-encoded images: Color Co-occurrence Feature (CCF) and Bit Pattern Feature (BPF). CCF is extracted from color quantizers produced by ODBTC, while BPF is based on a bit pattern codebook generated from ODBTC bitmap images. Experimental results show this approach provides superior retrieval accuracy compared to earlier CBIR methods.
Image Retrieval Based on its Contents Using Features ExtractionIRJET Journal
This document proposes a content-based image retrieval system using ordered-dither block truncation coding. The system extracts two image features - color co-occurrence features and bit pattern features - directly from the encoded image data to represent images. Experiments show the system retrieves similar images with promising accuracy compared to other methods. The system decomposes images into color quantizers and bitmaps using ordered-dither block truncation coding for low complexity feature extraction to represent images in the database. Queries return similar images based on similarity distances between feature vectors.
The content based image retrieval (CBIR) technique
is one of the most popular and evolving research areas of the
digital image processing. The goal of CBIR is to extract visual
content like colour, texture or shape, of an image automatically.
This paper proposes an image retrieval method that uses colour
and texture for feature extraction. This system uses the query by
example model. The system allows user to choose the feature on
the basis of which retrieval will take place. For the retrieval
based on colour feature, RGB and HSV models are taken into
consideration. Whereas for texture the GLCM is used for
extracting the textural features which then goes into Vector
Quantization phase to speed up the retrieval process.
This document summarizes image indexing and its features. It discusses that image indexing is used to retrieve similar images from a database based on extracted features like color, shape, and texture. Color features can be represented by models like RGB, HSV, and color histograms. Shape features include global properties like roundness and local features like edge segments. Texture is described using statistical, structural, and spectral approaches. Texture feature extraction methods discussed include standard wavelets, Gabor wavelets, and extracting features like entropy and standard deviation. The paper provides an overview of the different features used for image indexing and classification.
A Hybrid Approach for Content Based Image Retrieval SystemIOSR Journals
This document describes a hybrid approach for content-based image retrieval. It combines several spatial features - row sum, column sum, forward and backward diagonal sums - and histograms to represent images with feature vectors. Euclidean distance is used to calculate similarity between a query image's feature vector and those in the database. The approach is evaluated using precision-recall calculations on different image groups, showing the hybrid method performs best by combining multiple features.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
Automatic Image Annotation Using CMRM with Scene InformationTELKOMNIKA JOURNAL
Searching of digital images in a disorganized image collection is a challenging problem.
One step of image searching is automatic image annotation. Automatic image annotation refers
to the process of automatically assigning relevant text keywords to any given image, reflecting
its content. In the past decade many automatic image annotation methods have been proposed
and achieved promising result. However, annotation prediction from the methods is still far from
accurate. To tackle this problem, in this paper we propose an automatic annotation method
using relevance model and scene information. CMRM is one of automatic image annotation
method based on relevance model approach. CMRM method assumes that regions in an image
can be described using a small vocabulary of blobs. Blobs are generated from segmentation,
feature extraction, and clustering. Given a training set of images with annotations, this method
predicts the probability of generating a word given the blobs in an image. To improve annotation
prediction accuracy of CMRM, in this paper we utilize scene information incorporate with
CMRM. Our proposed method is called scene-CMRM. Global image region can be represented
by features which indicate type of scene shown in the image. Thus, annotation prediction of
CMRM could be more accurate based on that scene type. Our experiments showed that, the
methods provides prediction with better precision than CMRM does, where precision represents
the percentage of words that is correctly predicted.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Evaluation of Euclidean and Manhanttan Metrics In Content Based Image Retriev...IJERA Editor
This document evaluates the performance of the Euclidean and Manhattan distance metrics in a content-based image retrieval system. It finds that the Manhattan distance metric showed better precision than the Euclidean distance metric. The system uses color histograms and Gabor texture features to represent images. Color is represented in HSV color space and histograms of hue, saturation and value are used. Gabor filters are applied to capture texture at different scales and orientations. Distance between feature vectors is calculated using Euclidean and Manhattan distance formulas to find similar images from the database. The system was tested on a dataset of 1000 Corel images and Manhattan distance produced more relevant search results.
Color and texture based image retrievaleSAT Journals
Abstract Content-based image retrieval (CBIR) is an vital research area for manipulating bulky image databases and records. Alongside the conventional method where the images are searched on the basis of words, CBIR system uses visual contents to retrieve the images. In content based image retrieval systems texture and color features have been the primal descriptors. We use HSV color information and mean of the image as texture information. The performance of proposed scheme is calculated on the basis of precision, recall and accuracy. As an effect, the blend of color and texture features of the image provides strong feature set for image retrieval. Keywords: image retrieval, HSV color space, color histogram, image texture.
The document presents a method for detecting copy-move forgery in digital images using center-symmetric local binary pattern (CS-LBP). The key steps are:
1. The input image is converted to grayscale and divided into overlapping blocks.
2. CS-LBP features are extracted from each block to represent textures while being invariant to illumination and rotation. This reduces the feature dimensions compared to local binary pattern (LBP).
3. The distances between feature vectors are calculated and sorted lexicographically to group similar blocks together. Distances below a threshold indicate copied regions.
4. Post-processing with morphological operations fills holes and removes outliers to generate a mask of the forged regions.
The
Content based image retrieval using features extracted from halftoning-based ...LogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Content-Based Image Retrieval (CBIR) systems have been used for the searching of relevant images in various research areas. In CBIR systems features such as shape, texture and color are used. The extraction of features is the main step on which the retrieval results depend. Color features in CBIR are used as in the color histogram, color moments, conventional color correlogram and color histogram. Color space selection is used to represent the information of color of the pixels of the query image. The shape is the basic characteristic of segmented regions of an image. Different methods are introduced for better retrieval using different shape representation techniques; earlier the global shape representations were used but with time moved towards local shape representations. The local shape is more related to the expressing of result instead of the method. Local shape features may be derived from the texture properties and the color derivatives. Texture features have been used for images of documents, segmentation-based recognition,and satellite images. Texture features are used in different CBIR systems along with color, shape, geometrical structure and sift features.
The document presents a method for human action recognition using both RGB and depth data from an RGB-D sensor. Motion History Images (MHIs) are generated from RGB videos and Depth Motion Maps (DMMs) are generated from depth data after rotating the 3D point clouds. A 4-channel Deep Convolutional Neural Network is trained with one channel for MHIs and three channels for the rotated DMM views. Evaluated on the UTD-MHAD dataset, the proposed method achieves better recognition accuracy when fusing both RGB and depth modalities compared to using each individually.
- Content-based image retrieval (CBIR) searches for images based on visual features like color, texture, and shape rather than keywords.
- CBIR systems extract features from images to create metadata and use those features to calculate visual similarity between images.
- Relevance feedback allows users to provide feedback on initial search results to help the system recalculate feature weights and improve subsequent results.
The document discusses content-based image retrieval (CBIR) systems. It describes how CBIR systems use feature extraction to search large image databases based on visual content. The key components of CBIR systems are feature extraction, indexing, and system design. Feature extraction involves extracting information about images' colors, textures, shapes, and spatial locations. Effective features and indexing techniques are needed to make CBIR scalable for large image collections. Performance is evaluated based on how well systems return relevant images.
The students can learn about basics of image processing using matlab.
It explains the image operations with the help of examples and Matlab codes.
Students can fine sample images and .m code from the link given in slides.
This document summarizes a research paper that proposes a content-based image retrieval system using cascaded color and texture features. Color features are first extracted from images using statistical measures like mean, standard deviation, energy, entropy, skewness and kurtosis. Similarity to a query image is then measured using distance metrics. The top 150 most similar images are then analyzed to extract Haralick texture features. Similarity is again measured to retrieve the most relevant images. The paper finds that Canberra distance provides better retrieval results than other distance metrics like City Block and Minkowski.
Multi Resolution features of Content Based Image RetrievalIDES Editor
Many content based retrieval systems have been
proposed to manage and retrieve images on the basis of their
content. In this paper we proposed Color Histogram, Discrete
Wavelet Transform and Complex Wavelet Transform
techniques for efficient image retrieval from huge database.
Color Histogram technique is based on exact matching of
histogram of query image and database. Discrete Wavelet
transform technique retrieves images based on computation
of wavelet coefficients of subbands. Complex Wavelet
Transform technique includes computation of real and
imaginary part to extract the details from texture. The
proposed method is tested on COREL1000 database and
retrieval results have demonstrated a significant improvement
in precision and recall.
This document discusses various approaches to image indexing and retrieval, including using text descriptions, extracting color, shape and texture features, compressed image data, and spatial relationships. It describes common techniques like color histograms, shape representations, texture features, and using DCT, wavelet or VQ compression. An integrated approach is recommended to support both textual queries and pictorial similarity comparisons.
Literature Review on Content Based Image RetrievalUpekha Vandebona
This document summarizes a literature review on content-based image retrieval (CBIR). It discusses how CBIR uses computer vision techniques to automatically extract visual features from images for retrieval, unlike traditional concept-based methods that rely on metadata/text. The key visual features discussed are color, texture, and shape. A typical CBIR system architecture includes creating an image database, automatically extracting features, searching by example or semantics, and ranking results. Distance measures are used to compare image features and evaluate retrieval performance. Combining CBIR with concept-based techniques could improve image retrieval overall.
Empirical Coding for Curvature Based Linear Representation in Image Retrieval...iosrjce
The document presents a new approach called Linear Curvature Empirical Coding (LCEC) for image retrieval. LCEC aims to improve upon existing curvature-based coding approaches by linearly representing the curvature scale space plot and then applying empirical coding to select descriptive shape features. The linear representation considers variations across all smoothing factors rather than discarding information below a threshold. Empirical coding is used to select features based on variation density rather than just magnitude. The results show LCEC performs better than previous methods for image retrieval.
Feature Extraction in Content based Image Retrievalijcnes
This document discusses feature extraction methods for content-based image retrieval (CBIR). It describes Order Dither Block Truncation Coding (ODBTC), an image compression technique that can be used to extract image features without decoding. The proposed CBIR system extracts two features from ODBTC-encoded images: Color Co-occurrence Feature (CCF) and Bit Pattern Feature (BPF). CCF is extracted from color quantizers produced by ODBTC, while BPF is based on a bit pattern codebook generated from ODBTC bitmap images. Experimental results show this approach provides superior retrieval accuracy compared to earlier CBIR methods.
Image Retrieval Based on its Contents Using Features ExtractionIRJET Journal
This document proposes a content-based image retrieval system using ordered-dither block truncation coding. The system extracts two image features - color co-occurrence features and bit pattern features - directly from the encoded image data to represent images. Experiments show the system retrieves similar images with promising accuracy compared to other methods. The system decomposes images into color quantizers and bitmaps using ordered-dither block truncation coding for low complexity feature extraction to represent images in the database. Queries return similar images based on similarity distances between feature vectors.
Content Based Image Retrieval Approach Based on Top-Hat Transform And Modifie...cscpconf
In this paper a robust approach is proposed for content based image retrieval (CBIR) using texture analysis techniques. The proposed approach includes three main steps. In the first one, shape detection is done based on Top-Hat transform to detect and crop object part of the image. Second step is included a texture feature representation algorithm using color local binary patterns (CLBP) and local variance features. Finally, to retrieve mostly closing matching images to the query, log likelihood ratio is used. The performance of the proposed approach is evaluated using Corel and Simplicity image sets and it compared by some of other well-known approaches in terms of precision and recall which shows the superiority of the proposed approach. Low noise sensitivity, rotation invariant, shift invariant, gray scale invariant and low computational complexity are some of other advantages.
Image retrieval is the major innovations in the development of images. Mining of images is used to mine latest information from
the general collection of images. CBIR is the latest method in which our target images is to be extracted on the basis of specific features of
the specified image. The image can be retrieved in fast if it is clustered in an accurate and structured manner. In this paper, we have the
combined the theories of CBIR and analysis of features of CBIR systems.
Global Descriptor Attributes Based Content Based Image Retrieval of Query ImagesIJERA Editor
The need for efficient content-based image retrieval system has increased hugely. Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. Content based image retrieval (CBIR) is a promising approach because of its automatic indexing retrieval based on their semantic features and visual appearance. In this proposed system we investigate method for describing the contents of images which characterizes images by global descriptor attributes, where global features are extracted to make system more efficient by using color features which are color expectancy, color variance, skewness and texture feature correlation.
Retrieval of Images Using Color, Shape and Texture Features Based on Contentrahulmonikasharma
The current study deals with deriving of image feature descriptor by error diffusion based block truncation coding (EDBTC). The image feature descriptor is basically comprised by the two error diffusion block truncation coding, color quantizers and its equivalent bitmap image. The bitmap image distinguish the image edges and textural information of two color quantizers to signify the color allocation and image contrast derived by the Bit Pattern Feature and Color Co-occurrence Feature. Tentative outcome reveal the benefit of proposed feature descriptor as contrast to existing schemes in image retrieval assignment under normal and textural images. The Error-Diffusion Block Truncation Coding method compresses an image efficiently, and at the same time, its consequent compacted information flow can provides an efficient feature descriptor intended for operating image recovery and categorization. As a result, the proposed design preserves an effective candidate for real-time image retrieval applications.
CBIR of Batik Images using Micro Structure Descriptor on Android IJECEIAES
Batik is part of a culture that has long developed and known by the people of Indonesia and the world. However, the knowledge is only on the name of batik, not at a more detailed level, such as image characteristic and batik motifs. Batik motif is very diverse, different areas have their own motifs and patterns related to local customs and values. Therefore, it is important to introduce knowledge about batik motifs and patterns effectively and efficiently. So, we build CBIR batik using Micro-Structure Descriptor (MSD) method on Android platform. The data used consisted of 300 images with 50 classes with each class consists of six images. Performance test is held in three scenarios, which the data is divided as test data and data train, with the ratio of scenario 1 is 50%: 50%, scenario 2 is 70%, 30%, and scenario 3 is 80%: 20%. The best results are generated by scenario 3 with precision valur 65.67% and recall value 65.80%, which indicates that the use of MSD on the android platform for CBIR batik performs well.
WEB IMAGE RETRIEVAL USING CLUSTERING APPROACHEScscpconf
Image retrieval system is an active area to propose a new approach to retrieve images from the
large image database. In this concerned, we proposed an algorithm to represent images using
divisive based and partitioned based clustering approaches. The HSV color component and Haar wavelet transform is used to extract image features. These features are taken to segment an image to obtain objects. For segmenting an image, we used modified k-means clustering algorithm to group similar pixel together into K groups with cluster centers. To modify Kmeans, we proposed a divisive based clustering algorithm to determine the number of cluster and get back with number of cluster to k-means to obtain significant object groups. In addition, we also discussed the similarity distance measure using threshold value and object uniqueness to quantify the results.
The document discusses a method for compressing color images using block truncation coding (BTC) and genetic algorithms. BTC works by dividing images into blocks and quantizing each block to a high or low value based on the block's mean. This reduces quality issues with BTC. Color images have correlated red, green, and blue planes. The method uses a common bit plane optimized with genetic algorithms to represent all three color planes, improving quality and compression ratio compared to standard BTC and error diffused BTC. Experimental results showed the proposed method provided higher quality reconstructed images as measured by peak signal-to-noise ratio.
SEMANTIC IMAGE RETRIEVAL USING MULTIPLE FEATUREScscpconf
In Content Based Image Retrieval (CBIR) some problem such as recognizing the similar
images, the need for databases, the semantic gap, and retrieving the desired images from huge
collections are the keys to improve. CBIR system analyzes the image content for indexing,
management, extraction and retrieval via low-level features such as color, texture and shape.
To achieve higher semantic performance, recent system seeks to combine the low-level features
of images with high-level features that conation perceptual information for human beings.
Performance improvements of indexing and retrieval play an important role for providing
advanced CBIR services. To overcome these above problems, a new query-by-image technique
using combination of multiple features is proposed. The proposed technique efficiently sifts through the dataset of images to retrieve semantically similar images.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
CBIR Processing Approach on Colored and Texture Images using KNN Classifier a...IRJET Journal
This document presents a content-based image retrieval system that uses color and texture features. It uses a K-nearest neighbor classifier to classify images based on color features and extract texture features using log-Gabor filters. Images are then ranked based on their similarity to the query image using Spearman's rank correlation coefficient. The system is tested on a dataset of flag images to retrieve the most similar flags to a given query image based on color and texture features. Experimental results show that the combined approach of using classification, similarity measures and log-Gabor filtering for color and texture features provides better retrieval performance than methods using only wavelets or Gabor filters.
Halftoning-based BTC image reconstruction using patch processing with border ...TELKOMNIKA JOURNAL
This paper presents a new halftoning-based block truncation coding (HBTC) image reconstruction using sparse representation framework. The HBTC is a simple yet powerful image compression technique, which can effectively remove the typical blocking effect and false contour. Two types of HBTC methods are discussed in this paper, i.e., ordered dither block truncation coding (ODBTC) and error diffusion block truncation coding (EDBTC). The proposed sparsity-based method suppresses the impulsive noise on ODBTC and EDBTC decoded image with a coupled dictionary containing the HBTC image component and the clean image component dictionaries. Herein, a sparse coefficient is estimated from the HBTC decoded image by means of the HBTC image dictionary. The reconstructed image is subsequently built and aligned from the clean, i.e. non-compressed image dictionary and predicted sparse coefficient. To further reduce the blocking effect, the image patch is firstly identified as “border” and “non-border” type before applying the sparse representation framework. Adding the Laplacian prior knowledge on HBTC decoded image, it yields better reconstructed image quality. The experimental results demonstrate the effectiveness of the proposed HBTC image reconstruction. The proposed method also outperforms the former schemes in terms of reconstructed image quality.
A Review of Feature Extraction Techniques for CBIR based on SVMIJEEE
As with the advancement of multimedia technologies, users are not gratified with the conventional retrieval system techniques. So a application “Content Based Image Retrieval System” is introduced. CBIR is the application to retrieve the images or to search the digital images from the large database .The term “content” deals with the colour, shape, texture and all the information which is extracted from the image itself. This paper reviews the CBIR system which uses SVM classifier based algorithms for feature extraction phase.
A REVIEW ON IMAGE COMPRESSION USING HALFTONING BASED BTC ijcsity
In this paper scrutinizes image compression using Halftoning Based Block Truncation Coding for color image. Many algorithms were selected likely the original Block Truncation coding, Ordered Dither Block Truncation Coding, Error Diffusion Block Truncation Coding , and Dot Diffused Block Truncation Coding . These above techniques are divided image into non overlapping blocks. BTC acts as the basic compression technique but it exhibits two disadvantages such as the false contour and blocking effect. Hence halftoning based block truncation coding (HBTC) is used to overcome the two issues. Objective measures are used to evaluate the image degree of excellence such as Peak Signal to Noise Ratio, Mean Square Error, Structural Similarity Index and Compression Ratio. At the end, conclusions have shown that the Dot Diffused Block Truncation Coding algorithm outperforms the Block Truncation Coding as well as Error Diffusion Block Truncation Coding.
A Novel Method for Content Based Image Retrieval using Local Features and SVM...IRJET Journal
1) The document presents a novel approach for content-based image retrieval that uses local features like color, texture, and edges extracted from images.
2) It extracts these features and uses an SVM classifier to optimize retrieval results. This improves accuracy compared to other techniques that use only one content feature.
3) The proposed system is tested on parameters like accuracy, sensitivity, specificity, error rate, and retrieval time, and shows better performance than other methods.
Image search using similarity measures based on circular sectorscsandit
With growing number of stored image data, image sea
rch and image similarity problem become
more and more important. The answer can be solved b
y Content-Based Image Retrieval
systems. This paper deals with an image search usin
g similarity measures based on circular
sectors method. The method is inspired by human eye
functionality. The main contribution of the
paper is a modified method that increases accuracy
for about 8% in comparison with original
approach. Here proposed method has used HSB colour
model and median function for feature
extraction. The original approach uses RGB colour m
odel with mean function. Implemented
method was validated on 10 image categories where o
verall average precision was 67%
IMAGE SEARCH USING SIMILARITY MEASURES BASED ON CIRCULAR SECTORScscpconf
With growing number of stored image data, image search and image similarity problem become
more and more important. The answer can be solved by Content-Based Image Retrieval
systems. This paper deals with an image search using similarity measures based on circular
sectors method. The method is inspired by human eye functionality. The main contribution of the
paper is a modified method that increases accuracy for about 8% in comparison with original
approach. Here proposed method has used HSB colour model and median function for feature
extraction. The original approach uses RGB colour model with mean function. Implemented
method was validated on 10 image categories where overall average precision was 67%.
A SURVEY ON CONTENT BASED IMAGE RETRIEVAL USING MACHINE LEARNINGIRJET Journal
This document provides a literature review of recent research on content-based image retrieval using machine learning techniques. It summarizes 8 research papers that used approaches like convolutional neural networks, color histograms, deep learning, hashing functions and more to extract image features and retrieve similar images from databases. The goal of content-based image retrieval is to find images that are semantically similar to a query image based on visual features.
This document discusses various techniques for image retrieval, including text-based, content-based, and hybrid approaches. Content-based image retrieval (CBIR) extracts visual features like color, texture, shape from images and is able to retrieve similar images to a query image. CBIR systems segment images, extract features, search databases, and return results. CBIR has advantages over text-based retrieval but challenges remain around the semantic gap between low-level features and high-level concepts. The document also discusses evaluating retrieval performance and promising future research directions like reducing the semantic gap.
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Improving Performance of Multileveled BTC Based CBIR Using Sundry Color Spaces
1. Dr. H.B. Kekre, Sudeep Thepade & Shrikant Sanas
International Journal of Image Processing (IJIP), Volume (4): Issue (6) 620
Improving Performance of Multileveled BTC Based CBIR Using
Sundry Color Spaces
Dr. H. B. Kekre hbkekre@yahoo.com
Senior Professor,
MPSTME, SVKM’s NMIMS University,
Mumbai-56, India
Sudeep Thepade sudeepthepade@gmail.com
Associate Professor and Ph.D. Research Scholar,
MPSTME,SVKM’s NMIMS University,
Mumbai-56, India
Shrikant P. Sanas shrikant_sanas@yahoo.co.in
M-tech Student
MPSTME,SVKM’s NMIMS University,
Mumbai-56, India
Abstract
The paper presents an extension of content based image retrieval (CBIR)
techniques based on multilevel Block Truncation Coding (BTC) using nine sundry
color spaces. Block truncation coding based features is one of the CBIR methods
proposed using color features of image. The approach basically considers red,
green and blue planes of an image to compute feature vector. This BTC based
CBIR can be extended as multileveled BTC for performance improvement in
image retrieval. The paper extends the multileveled BTC using RGB color space
to other nine color spaces. The CBIR techniques like BTC Level-1, BTC Level-2,
BTC Level-3 and BTC Level-4 are applied using various color spaces to analyze
and compare their performances. The CBIR techniques are tested on generic
image database of 1000 images spread across 11 categories. For each CBIR
technique, 55 queries (5 per category) are fired on extended Wang generic
image database to compute average precision and recall for all queries. The
results have shown the performance improvement (ie., higher precision and
recall values) with BTC-CBIR methods using luminance-chrominance color
spaces (YCgCb, Kekre’s LUV, YUV, YIQ, YCbCr) as compared to non-
luminance (RGB, HSI, HSV, rgb , XYZ) Color spaces. The performance of
multileveled BTC-CBIR increases gradually with increase in level up to certain
extent (Level 3) and then increases slightly due to voids being created at higher
levels. In all levels of BTC Kekre’s LUV color space gives best performance.
Keywords: Content Based Image Retrieval (CBIR), BTC, Color Spaces.
1. INTRODUCTION
From ancient era, images play an important role in human communication. It is basic and
common way to express the information. Today with advancement in information and
2. Dr. H.B. Kekre, Sudeep Thepade & Shrikant Sanas
International Journal of Image Processing (IJIP), Volume (4): Issue (6) 621
communication technology most of the information is digitized. Large amount of digital data is
generated, transmitted, stored, analyzed and accessed. Mostly, information is in the form of
multimedia such as digital images, audio, video, graphics [6]-8. Large numbers of images are
generated from various sources on a daily basis. Such images occupy lot of space and are very
challenging to search and retrieve from very large image pool. The need for efficient retrieval of
images has been recognized by managers and users of large image collections. Efficient indexing
techniques for the retrieval of best matching image from a huge database of images are being
developed. Content based image retrieval gives efficient solution to these problems [18,19].
Content Based Image Retrieval (CBIR) is used to provide a high percentage of relevant images in
response to the query image [12]. The goal of an image retrieval system is to retrieve a set of
matching images from an image database [21].
A Content Based Image Retrieval (CBIR) technique takes an image as an input to query and
outputs number of matching images to the query image [11]. In CBIR technique, features are
used to represent the image content. The features are extracted automatically and there is no
manual intervention, and thus eliminating the dependency on humans in the feature extraction
stage [10]. The typical CBIR system performs two major tasks. The first one is feature extraction
(FE), where a set of features, forming feature vector, is generated to accurately represent the
content of each image in the database. A feature vector is much smaller in size than the original
image. The second task is similarity measurement (SM), where a distance between the query
image and each image in the database using their feature vectors (signatures) is computed so
that the top “closest” images retrieved [3], [13], [14], [15].
Many current CBIR system use Euclidean distance [5] on the extracted feature set as a similarity
measure. The Direct Euclidian distance between image P and query image Q can be given as
equation. where Vpi and Vqi are the feature vectors of image P and query image Q respectively
with size ‘n’.
∑=
−=
n
i
VqiVpiED
1
2
)( (1)
Some of important applications for CBIR technology could be identified as art galleries,
museums, archaeology, architecture design, geographic information systems, weather forecast,
medical imaging , trademark databases, criminal investigations, image search on the Internet.
The thirst of a better and faster image retrieval technique is increasing day by day. The paper
discusses the performance improvement in multileveled BTC based CBIR techniques [1] using
various color spaces. In all ten different color spaces including RGB color space are considered
here with four different levels of BTC for feature extraction resulting into total 40 CBIR methods.
2. BLOCK TRUNCATION CODING (BTC)
Block truncation coding (BTC) is a relatively simple image coding technique developed in the
early years of digital imaging more than 29 years ago. Although it is a simple technique, BTC has
played an important role in the history of digital image coding in the sense that many advanced
coding techniques have been developed based on BTC or inspired by the success of BTC [2].
Block Truncation Coding (BTC) was first developed in 1979 for grayscale image coding [2]. The
method first computes the mean pixel value of the whole block and then each pixel in that block is
compared to the block mean. If a pixel is greater than or equal to the block mean, the
corresponding pixel position of the bitmap will have a value of 1 otherwise it will have a value of 0.
Two mean pixel values one for the pixels greater than or equal to the block mean and the other
for the pixels smaller than the block mean are also calculated. At decoding stage, the small
blocks are decoded one at a time. For each block, the pixel positions where the corresponding
bitmap has a value of 1 is replaced by one mean pixel value and those pixel positions where the
corresponding bitmap has a value of 0 is replaced by another mean pixel value.
3. Dr. H.B. Kekre, Sudeep Thepade & Shrikant Sanas
International Journal of Image Processing (IJIP), Volume (4): Issue (6) 622
3. CBIR USING MULTILEVELED BLOCK TRUNCATION CODING
The block truncation coding (BTC) technique can be extended to higher levels by considering
multiple threshold values to divide the image pixels into higher (upper) and less than or equal to
(lower) threshold. The image pixel data is thus divided in to multiple clusters and per cluster the
mean value is taken as part of feature vector. At BTC Level 1 only one threshold value is used to
divide the pixel data to get two clusters and respective means of these clusters as upper mean
and lower mean are computed, resulting in to feature vector of size six (two values per color
plane). In next level each cluster can be further divided into two parts with respect to it’s mean
value resulting into total four clusters per color plane to get feature vector of size twelve (four per
plane). Thus BTC can be extended to multiple levels to get BTC Level 2, BTC Level 3, etc. The
feature vector extraction for CBIR using multileveled BTC with RGB color space is explained in
section A, B and C here.
A) CBIR using BTC-RGB-Level-1 (BTC-RGB-6) [3,4,9,17]
In original BTC we divide the image into R, B, and G components and compute the mean value of
each color component as individual color. Let I(m,n)=[R(m,n), G(m,n), B(m,n)] be the color image
image of size mxn. Let the thresholds be MR, MG and MB, which could be computed as per the
equations 2, 3 and 4.
∑∑= =
=
m
i
n
j
jiR
nm
MR
1 1
),(
*
1 (2)
∑∑= =
=
m
i
n
j
jiG
nm
MG
1 1
),(
*
1
(3)
∑∑= =
=
m
i
n
j
jiB
nm
MB
1 1
),(
*
1
(4)
Here three binary bitmaps will be computed as BMr, BMg and BMb. If a pixel in each component
(R, G, and B) is greater than or equal to the respective threshold, the corresponding pixel position
of the bitmap will have a value of ‘1’ otherwise it will have a value of ‘0’.
MRjiRif
MRjiRif
jiBMr
<=
>
=
),(...,....0
),(....,1
{),( (5)
MGjiGif
MGjiGif
jiBMg
<=
>
=
),(...,....0
),(....,1
{),( (6)
MBjiBif
MBjiBif
jiBMb
<=
>
=
),(...,....0
),(....,1
{),(
(7)
),(*),(*
),(
1
1 1
1 1
jiRjiBMr
jiBMr
UR
m
i
n
j
m
i
n
j
∑∑
∑∑ = =
= =
= (8)
4. Dr. H.B. Kekre, Sudeep Thepade & Shrikant Sanas
International Journal of Image Processing (IJIP), Volume (4): Issue (6) 623
Two mean colors one for the pixels greater than or equal to the threshold and other for the pixels
smaller than the threshold are also calculated [15]. The upper mean color UM(UR, UG, UB) is
given as equations 8, 9 and 10.
And the Lower Mean LM= (LR, LG, LB) is computed as following equations 11, 12 and 13.
),(*)},(1{*
),(*
1
1 1
1 1
jiRjiBMr
jiBMrnm
LR
m
i
n
j
m
i
n
j
∑∑
∑∑ = =
= =
−
−
=
(11)
),(*)},(1{*
),(*
1
1 1
1 1
jiGjiBMg
jiBMgnm
LG
m
i
n
j
m
i
n
j
∑∑
∑∑ = =
= =
−
−
=
(12)
),(*)},(1{*
),(*
1
1 1
1 1
jiBjiBMb
jiBMbnm
LB
m
i
n
j
m
i
n
j
∑∑
∑∑ = =
= =
−
−
=
(13)
These Upper Mean and Lower Mean together will form a feature vector or signature of the image.
For every image stored in the database these feature vectors are computed and stored in feature
vector table.
B) CBIR using BTC- RGB- Level 2 (BTC-RGB-12)[1]
In BTC-RGB-Level 2 the image data is divided into 12 parts using the six means obtained in BTC-
RGB-Level 1. Here the bitmap are prepared using upper and lower mean values of individual
color components. For red color component the bitmap ‘BMUR’ and ‘BMLR’ are generated as
given in equations 14 and 15. Similarly for green color component ‘BMUG’ & ‘BMLR’ and for blue
colour components ‘BMUB’ & ‘BMLB’ can be generated.
URjiRif
URjiRif
jiBMUR
<=
>
=
),(...,....0
),(....,1
{),( (14)
LRjiRif
LRjiRif
jiBMLR
<=
>
=
),(...,....0
),(....,1
{),( (15)
Using this bitmap the two mean colour per bitmap one for the pixels greater than or equal to the
threshold and other for the pixels smaller than the threshold are calculated [15]. The red plane of
the image is divided into two parts as upper red image and lower red image as given by
equations 16 and 17.
....1.,...1.),..,(*),(),( njandmiforjiBMrjiRjiIur === (16)
....1.,...1.)],..,(1[*),(),( njandmiforjiBMrjiRjiIlr ==−= (17)
),(*),(*
),(
1
1 1
1 1
jiGjiBMg
jiBMg
UG
m
i
n
j
m
i
n
j
∑∑
∑∑ = =
= =
= (9)
),(*),(*
),(
1
1 1
1 1
jiBjiBMb
jiBMb
UB
m
i
n
j
m
i
n
j
∑∑
∑∑ = =
= =
= (10)
5. Dr. H.B. Kekre, Sudeep Thepade & Shrikant Sanas
International Journal of Image Processing (IJIP), Volume (4): Issue (6) 624
The upper mean color UM (UUR, ULR, UUG, ULG, UUB, ULB) are given as follows.
),(*),(*
),(
1
1 1
1 1
jiIurjiBMUR
jiBMUR
UUR
m
i
n
j
m
i
n
j
∑∑
∑∑ = =
= =
=
(18)
),(*),(*
),(
1
1 1
1 1
jiIlrjiBMLR
jiBMLR
ULR
m
i
n
j
m
i
n
j
∑∑
∑∑ = =
= =
=
(19)
And the first two components of Lower Mean LM= (LUR, LLR, LUG, LLG, LUB, LLB) are
computed using following equations
),(*)},(1{*
),(
1
1 1
1 1
jiIurjiBMUR
jiBMUR
LUR
m
i
n
j
m
i
n
j
∑∑
∑∑ = =
= =
−= (20)
),(*)},(1{*
),(
1
1 1
1 1
jiIlrjiBMLR
jiBMLR
LLR
m
i
n
j
m
i
n
j
∑∑
∑∑ = =
= =
−= (21)
These Upper Mean and Lower Mean values together will form a feature vector for BTC-12. For
every image stored in the database these feature vectors are computed and stored in feature
vector table.
C) CBIR using BTC-RGB-Level 3 (BTC-RGB-24)[1] and BTC-RGB-Level 4 (BTC-RGB-48)
Similarly the feature vector for BTC-24 can be found by extending the BTC till level 3. Each plane
will give the 8 elements of feature vector. For Red plane we get (UUUR, LUUR, ULUR, LLUR,
UULR, LULR, ULLR, LLLR). Also feature vector of size 48 can be generated by extending this
process of BTC to next level.
4. COLOR SPACES IN CBIR USING MULTILEVELED BTC
Just as discussed in section 3 for RGB color space, the CBIR using multileveled BTC can be
used with other color spaces. Here in all ten color spaces like RGB, HSV[20], XYZ[20], rgb[20],
HSI[20], Kekre’s LUV [3], YCbCr[17], YUV[9], YIQ[20], Kekre’s YCgCb[20] are considered. The
ten color spaces along with BTC extended to four levels resul into total 40 CBIR methods. These
color spaces can mainly be divided into two categories as luminance-chrominance color spaces
(Kekre’s LUV, YCbCr, YUV, YIQ, Kekre’s YCgCb) and nom-luminance color spaces (RGB, HSI,
HSV, XYZ and rgb).
5. IMPLEMENTATION
The implementation of these CBIR techniques is done using MATLAB 7.0. The CBIR techniques
are tested on the augmented Wang [15] image database of 1000 variable size images spread
across 11 categories of human beings, animals, natural scenery and man-made things. To
compare various techniques in various color space, performance is evaluated based on precision
and recall. The efficiency of CBIR technique is evaluated based on accuracy, stability and speed.
To assess the retrieval effectiveness, we have used the precision and recall as statistical
comparison parameters for the BTC-6, BTC-12, BTC-24 and BTC-48 techniques of CBIR on all
color spaces. The standard definitions of these two measures are given by following equations.
edes_retriever_of_imagTotal_numb
ievedmages_retrrelevant_iNumber_of_
Precesion =
(22)
6. Dr. H.B. Kekre, Sudeep Thepade & Shrikant Sanas
International Journal of Image Processing (IJIP), Volume (4): Issue (6) 625
ases_in_databvent_imageer_of_releTotal_numb
ievedmages_retrrelevant_iNumber_of_
Recall =
(23)
6. RESULTS AND DISCUSSION
The methods BTC-6, BTC-12, BTC-24 and BTC-48 are applied to the image database of 1000
images using various color spaces. The resulting 40 CBIR methods are tested using 55 random
queries (5 per image category). The average precision and recall values of all these queries per
CBIR methods are computed and considered for performance comparison.
Figure 1 shows the average precision and recall values plotted against number of retrieved
images for RGB-BTC-CBIR at level 1 (RGB-BTC-6), level 2 (RGB-BTC-12), level 3 (RGB-BTC-
24) and level 4 (RGB-BTC-48). The conclusion that ‘higher the level of BTC used better the
performance of CBIR is’ can be drawn from the given graph as indicated by higher precision and
recall values in BTC 48 and BTC 24 as compared to BTC-12 and BTC-6.
However the distinction in the performance of all these techniques is not very clear. The height of
crossover point of precision and recall curves plays very important role in performance
comparison of CBIR methods. Ideally this crossover point height should be one. Higher the value
of this crossover point better the performance is.
Figure 2a shows the zoomed version of graphs in figure 1 for crossover points of precision and
recall curves. Figure 2 b shows the bar graphs indicating the heights of crossover points of
precision-recall curves of respective CBIR methods shown in figure 2a. From this figure 2b it can
be observed that the best performance (Highest precision-recall crossover point value) is given
by BTC-RGB-48. The increase in performance from BTC-RGB-6 to BTC-RGB-24 is gradual in
nature while the performances of BTC-RGB-24 and BTC-RGB48 have very slight difference. So
the conclusion that multileveled BTC helps to improve the performance of CBIR methods up to
BTC-Level 3 (BTC-24) can be drawn from these observations.
Figure 3 gives the comparison of crossover points of the multileveled BTC-CBIR techniques per
color space. In all color spaces increasing the BTC level helps in improving the performance of
CBIR as indicated by higher crossover point values.
Figure 4 gives the comparison of crossover points for all color spaces per level of BTC-CBIR
techniques. In all levels of BTC-CBIR luminance-chrominance color spaces (YCgCb, Kekre’s
LUV, YUV, YIQ, YCbCr) better than non-luminance (RGB, HSI, HSV, rgb, XYZ) Color spaces and
in all Kekre’s LUV gives best performance.
7. Dr. H.B. Kekre, Sudeep Thepade & Shrikant Sanas
International Journal of Image Processing (IJIP), Volume (4): Issue (6) 626
FIGURE 1 : Precision & Recall plotted against for RGB color space.
FIGURE2.A: Crossover points of Precision-Recall plotted against number of retrieved images.
8. Dr. H.B. Kekre, Sudeep Thepade & Shrikant Sanas
International Journal of Image Processing (IJIP), Volume (4): Issue (6) 627
FIGURE 2.B : Performance comparison of discussed CBIR methods using Precision-Recall crossover
points for RGB color sapce.
FIGURE 3: Performance Comparison between different levels of BTC for respective color spaces.
9. Dr. H.B. Kekre, Sudeep Thepade & Shrikant Sanas
International Journal of Image Processing (IJIP), Volume (4): Issue (6) 628
FIGURE 4: Performance Comparison between different color spaces for respective level of BTC.
7. CONCLUSION
The performance of CBIR system depends on the precision and recall. Quite often the crossover
point of precision and recall is taken as criteria for judging the performance of CBIR technique on
various Color spaces. Kekre’s LUV gives better result and the values are 0.4258, 0.448, 0.462,
0.46495 for BTC-6, BTC-12, BTC-24 and BTC-48 respectively. The results have shown that, the
performance improvement (i.e. higher precision and recall values) in Luminous & Chrominance
Color spaces with BTC-CBIR methods compared to Non-Luminous &Non-Chrominance Color
spaces are lower. Performance improves with increased level of BTC. Up to level -3 gradual
increases in performance with increasing level is observed while the difference of performance in
level-3 and level-4 is negligible due to voids being created at higher levels.
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International Journal of Image Processing (IJIP), Volume (4): Issue (6) 630
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