Content based image retrieval (CBIR) is the task of searching digital images from a large database based on the extraction of features, such as color, texture and shape of the image. Most of the research in CBIR has been carried out with complete queries which were present in the database. This paper investigates utility of CBIR techniques for retrieval of incomplete and distorted queries. Studies were made in two categories of the query: first is complete and second is incomplete. The query image is considered to be distorted or incomplete image if it has some missing information, some undesirable objects, blurring, noise due to disturbance at the time of image acquisition etc. Color (hue, saturation and value (HSV) color space model) and shape (moment invariants and Fourier descriptor) features are used to represent the image. The algorithm was tested on database consisting of 1875 images. The results show that retrieval accuracy of incomplete queries is highly increased by fusing color and shape features giving precision of 79.87%. MATLAB ® 7.01 and its image processing toolbox have been used to implement the algorithm.
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.
Amalgamation of contour, texture, color, edge, and spatial features for effic...eSAT Journals
Abstract From the past few years, Content based image retrieval (CBIR) has been a progressive and curious research area. Image retrieval is a process of extraction of the set of images from the available image database resembling the query image. Many CBIR techniques have been proposed for relevant image recoveries. However most of them are based on a particular feature extraction like texture based recovery, color based retrieval system etc. Here in this paper we put forward a novel technique for image recovery based on the integration of contour, texture, color, edge, and spatial features. Contourlet decomposition is employed for the extraction of contour features such as energy and standard deviation. Directionality and anisotropy are the properties of contourlet transformation that makes it an efficient technique. After feature extraction of query and database images, similarity measurement techniques such as Squared Euclidian and Manhattan distance were used to obtain the top N image matches. The simulation results in Matlab show that the proposed technique offers a better image retrieval. Satisfactory precision-recall rate is also maintained in this method. Keywords: Contourlet Decomposition, Local Binary Pattern, Squared Euclidian Distance, Manhattan Distance
A comparative study on content based image retrieval methodsIJLT EMAS
Content-based image retrieval (CBIR) is a method of
finding images from a huge image database according to persons’
interests. Content-based here means that the search involves
analysis the actual content present in the image. As database of
images is growing daybyday, researchers/scholars are searching
for better techniques for retrieval of images maintaining good
efficiency. This paper presents the visual features and various
ways for image retrieval from the huge image database.
EFFICIENT IMAGE RETRIEVAL USING REGION BASED IMAGE RETRIEVALsipij
Early image retrieval techniques were based on textual annotation of images. Manual annotation of images
is a burdensome and expensive work for a huge image database. It is often introspective, context-sensitive
and crude. Content based image retrieval, is implemented using the optical constituents of an image such
as shape, colour, spatial layout, and texture to exhibit and index the image. The Region Based Image
Retrieval (RBIR) system uses the Discrete Wavelet Transform (DWT) and a k-means clustering algorithm
to segment an image into regions. Each region of the image is represented by a set of optical
characteristics and the likeness between regions and is measured using a particular metric function on
such characteristics
Object Shape Representation by Kernel Density Feature Points Estimator cscpconf
This paper introduces an object shape representation using Kernel Density Feature Points
Estimator (KDFPE). In this method we obtain the density of feature points within defined rings
around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to
the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of
image representation shows improved retrieval rate when compared to Density Histogram
Feature Points (DHFP) method. Analytic analysis is done to justify our method and we compared our results with object shape representation by the Density Histogram of Feature Points (DHFP) to prove its robustness.
Content Based Image Retrieval using Color Boosted Salient Points and Shape fe...CSCJournals
Salient points are locations in an image where there is a significant variation with respect to a chosen image feature. Since the set of salient points in an image capture important local characteristics of that image, they can form the basis of a good image representation for content-based image retrieval (CBIR). Salient features are generally determined from the local differential structure of images. They focus on the shape saliency of the local neighborhood. Most of these detectors are luminance based which have the disadvantage that the distinctiveness of the local color information is completely ignored in determining salient image features. To fully exploit the possibilities of salient point detection in color images, color distinctiveness should be taken into account in addition to shape distinctiveness. This paper presents a method for salient points determination based on color saliency. The color and texture information around these points of interest serve as the local descriptors of the image. In addition, the shape information is captured in terms of edge images computed using Gradient Vector Flow fields. Invariant moments are then used to record the shape features. The combination of the local color, texture and the global shape features provides a robust feature set for image retrieval. The experimental results demonstrate the efficacy of the method.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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.
Amalgamation of contour, texture, color, edge, and spatial features for effic...eSAT Journals
Abstract From the past few years, Content based image retrieval (CBIR) has been a progressive and curious research area. Image retrieval is a process of extraction of the set of images from the available image database resembling the query image. Many CBIR techniques have been proposed for relevant image recoveries. However most of them are based on a particular feature extraction like texture based recovery, color based retrieval system etc. Here in this paper we put forward a novel technique for image recovery based on the integration of contour, texture, color, edge, and spatial features. Contourlet decomposition is employed for the extraction of contour features such as energy and standard deviation. Directionality and anisotropy are the properties of contourlet transformation that makes it an efficient technique. After feature extraction of query and database images, similarity measurement techniques such as Squared Euclidian and Manhattan distance were used to obtain the top N image matches. The simulation results in Matlab show that the proposed technique offers a better image retrieval. Satisfactory precision-recall rate is also maintained in this method. Keywords: Contourlet Decomposition, Local Binary Pattern, Squared Euclidian Distance, Manhattan Distance
A comparative study on content based image retrieval methodsIJLT EMAS
Content-based image retrieval (CBIR) is a method of
finding images from a huge image database according to persons’
interests. Content-based here means that the search involves
analysis the actual content present in the image. As database of
images is growing daybyday, researchers/scholars are searching
for better techniques for retrieval of images maintaining good
efficiency. This paper presents the visual features and various
ways for image retrieval from the huge image database.
EFFICIENT IMAGE RETRIEVAL USING REGION BASED IMAGE RETRIEVALsipij
Early image retrieval techniques were based on textual annotation of images. Manual annotation of images
is a burdensome and expensive work for a huge image database. It is often introspective, context-sensitive
and crude. Content based image retrieval, is implemented using the optical constituents of an image such
as shape, colour, spatial layout, and texture to exhibit and index the image. The Region Based Image
Retrieval (RBIR) system uses the Discrete Wavelet Transform (DWT) and a k-means clustering algorithm
to segment an image into regions. Each region of the image is represented by a set of optical
characteristics and the likeness between regions and is measured using a particular metric function on
such characteristics
Object Shape Representation by Kernel Density Feature Points Estimator cscpconf
This paper introduces an object shape representation using Kernel Density Feature Points
Estimator (KDFPE). In this method we obtain the density of feature points within defined rings
around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to
the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of
image representation shows improved retrieval rate when compared to Density Histogram
Feature Points (DHFP) method. Analytic analysis is done to justify our method and we compared our results with object shape representation by the Density Histogram of Feature Points (DHFP) to prove its robustness.
Content Based Image Retrieval using Color Boosted Salient Points and Shape fe...CSCJournals
Salient points are locations in an image where there is a significant variation with respect to a chosen image feature. Since the set of salient points in an image capture important local characteristics of that image, they can form the basis of a good image representation for content-based image retrieval (CBIR). Salient features are generally determined from the local differential structure of images. They focus on the shape saliency of the local neighborhood. Most of these detectors are luminance based which have the disadvantage that the distinctiveness of the local color information is completely ignored in determining salient image features. To fully exploit the possibilities of salient point detection in color images, color distinctiveness should be taken into account in addition to shape distinctiveness. This paper presents a method for salient points determination based on color saliency. The color and texture information around these points of interest serve as the local descriptors of the image. In addition, the shape information is captured in terms of edge images computed using Gradient Vector Flow fields. Invariant moments are then used to record the shape features. The combination of the local color, texture and the global shape features provides a robust feature set for image retrieval. The experimental results demonstrate the efficacy of the method.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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.
A COMPARATIVE ANALYSIS OF RETRIEVAL TECHNIQUES IN CONTENT BASED IMAGE RETRIEVALcscpconf
Basic group of visual techniques such as color, shape, texture are used in Content Based Image Retrievals (CBIR) to retrieve query image or sub region of image to find similar images in image database. To improve query result, relevance feedback is used many times in CBIR to help user to express their preference and improve query results. In this paper, a new approach for image retrieval is proposed which is based on the features such as Color Histogram, Eigen Values and Match Point. Images from various types of database are first identified by using edge detection techniques .Once the image is identified, then the image is searched in the particular database, then all related images are displayed. This will save the retrieval time. Further to retrieve the precise query image, any of the three techniques are used and comparison is done w.r.t. average retrieval time. Eigen value technique found to be the best as compared with other two techniques.
A comparative analysis of retrieval techniques in content based image retrievalcsandit
Basic group of visual techniques such as color, shape, texture are used in Content Based Image
Retrievals (CBIR) to retrieve query image or sub region of image to find similar images in
image database. To improve query result, relevance feedback is used many times in CBIR to
help user to express their preference and improve query results. In this paper, a new approach
for image retrieval is proposed which is based on the features such as Color Histogram, Eigen
Values and Match Point. Images from various types of database are first identified by using
edge detection techniques .Once the image is identified, then the image is searched in the
particular database, then all related images are displayed. This will save the retrieval time.
Further to retrieve the precise query image, any of the three techniques are used and
comparison is done w.r.t. average retrieval time. Eigen value technique found to be the best as
compared with other two techniques.
The development of multimedia system technology in Content based Image Retrieval (CBIR) System is
one in every of the outstanding area to retrieve the images from an oversized collection of database. The feature
vectors of the query image are compared with feature vectors of the database images to get matching images.It is
much observed that anyone algorithm isn't beneficial in extracting all differing kinds of natural images. Thus an
intensive analysis of certain color, texture and shape extraction techniques are allotted to spot an efficient CBIR
technique that suits for a selected sort of images. The Extraction of an image includes feature description and
feature extraction. During this paper, we tend to projected Color Layout Descriptor (CLD), grey Level Co-
Occurrences Matrix (GLCM), Marker-Controlled Watershed Segmentation feature extraction technique that
extract the matching image based on the similarity of Color, Texture and shape within the database. For
performance analysis, the image retrieval timing results of the projected technique is calculated and compared
with every of the individual feature.
Face skin color based recognition using local spectral and gray scale featureseSAT Journals
Abstract Human face conveys more information about identity of person. Human face recognition is one of the most challenging problem and it can be used in many applications at different security places in airports, defense and banking sectors etc.In this work used colored features obtained from color segmentation because in real time scenario color provides the more information than gray scale image but it has a drawback. To overcome this drawback gray scale feature extracted from co-occurrence matrix of an image and for efficient face recognition of human Face under different illumination conditions spectral features can be extracted from face texture. These three feature vectors concatenated into a single feature vector and applied Lenc-Kral matching technique to measure similarity between the database and query image, the similarity is high then face is recognized. Keywords: Face recognition, illumination condition, local texture features, color segmentation.
PDE BASED FEATURES FOR TEXTURE ANALYSIS USING WAVELET TRANSFORMIJCI JOURNAL
In the present paper, a novel method of partial differential equation (PDE) based features for texture
analysis using wavelet transform is proposed. The aim of the proposed method is to investigate texture
descriptors that perform better with low computational cost. Wavelet transform is applied to obtain
directional information from the image. Anisotropic diffusion is used to find texture approximation from
directional information. Further, texture approximation is used to compute various statistical features.
LDA is employed to enhance the class separability. The k-NN classifier with tenfold experimentation is
used for classification. The proposed method is evaluated on Brodatz dataset. The experimental results
demonstrate the effectiveness of the method as compared to the other methods in the literature.
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
A binarization technique for extraction of devanagari text from camera based ...sipij
This paper presents a binarization method for camera based natural scene (NS) images based on edge
analysis and morphological dilation. Image is converted to grey scale image and edge detection is carried
out using canny edge detection. The edge image is dilated using morphological dilation and analyzed to
remove edges corresponding to non-text regions. The image is binarized using mean and standard
deviation of edge pixels. Post processing of resulting images is done to fill gaps and to smooth text strokes.
The algorithm is tested on a variety of NS images captured using a digital camera under variable
resolutions, lightening conditions having text of different fonts, styles and backgrounds. The results are
compared with other standard techniques. The method is fast and works well for camera based natural
scene images.
An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance FeedbackIJMIT JOURNAL
Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database. Image contents are plays significant role for image retrieval. There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design. Each image has three contents such as: color, texture and shape features. Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results. Color histogram and texture features have potential to retrieve similar images on the basis of their properties. As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query. To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solutio
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.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Noise tolerant color image segmentation using support vector machineeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Query Image Searching With Integrated Textual and Visual Relevance Feedback f...IJERA Editor
There are many researchers who have studied the relevance feedback in the literature of content based image
retrieval (CBIR) community, but none of CBIR search engines support it because of scalability, effectiveness
and efficiency issues. In this, we had implemented an integrated relevance feedback for retrieving of web
images. Here, we had concentrated on integration of both textual features (TF) and visual features (VF) based
relevance feedback (RF), simultaneously we also tested them individually. The TFRF employs and effective
search result clustering (SRC) algorithm to get salient phrases. Then a new user interface (UI) is proposed to
support RF. Experimental results show that the proposed algorithm is scalable, effective and accurated
Fpga implementation of image segmentation by using edge detection based on so...eSAT Journals
Abstract In this paper, we present the method of “FPGA implementation of image segmentation by using edge detection based on the sobel edge operator” .due to advancement in computer vision it can be implemented in fpga based architecture. image segmentation separates an image into component regions and object. Segmentation needs to segment the object from the background to read image properly and identify the image carefully. Edge detection is fundamental tool for image segmentation. Sobel edge operator, which is very popular edge detection algorithms, is considered in this work. Sobel method uses the derivative approximation to find edge and perform 2-D spatial gradient measurement for images uses horizontal and vertical gradient matrices .The fpga device providing good performance of integrated circuit platform for research and development. The compact structure of image segmentation into edge detection can be implemented in MAT LAB using VHDL code and the waveform is shown in the model sim.. Keywords: VLSI, FPGA, image segmentation, sobel edge operators, edge detection pixel, mat lab.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A New Method for Indoor-outdoor Image Classification Using Color Correlated T...CSCJournals
In this paper a new method for indoor-outdoor image classification is presented; where the concept of Color Correlated Temperature is used to extract distinguishing features between the two classes. In this process, using Hue color component, each image is segmented into different color channels and color correlated temperature is calculated for each channel. These values are then incorporated to build the image feature vector. Besides color temperature values, the feature vector also holds information about the color formation of the image. In the classification phase, KNN classifier is used to classify images as indoor or outdoor. Two different datasets are used for test purposes; a collection of images gathered from the internet and a second dataset built by frame extraction from different video sequences from one video capturing device. High classification rate, compared to other state of the art methods shows the ability of the proposed method for indoor-outdoor image classification.
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.
A COMPARATIVE ANALYSIS OF RETRIEVAL TECHNIQUES IN CONTENT BASED IMAGE RETRIEVALcscpconf
Basic group of visual techniques such as color, shape, texture are used in Content Based Image Retrievals (CBIR) to retrieve query image or sub region of image to find similar images in image database. To improve query result, relevance feedback is used many times in CBIR to help user to express their preference and improve query results. In this paper, a new approach for image retrieval is proposed which is based on the features such as Color Histogram, Eigen Values and Match Point. Images from various types of database are first identified by using edge detection techniques .Once the image is identified, then the image is searched in the particular database, then all related images are displayed. This will save the retrieval time. Further to retrieve the precise query image, any of the three techniques are used and comparison is done w.r.t. average retrieval time. Eigen value technique found to be the best as compared with other two techniques.
A comparative analysis of retrieval techniques in content based image retrievalcsandit
Basic group of visual techniques such as color, shape, texture are used in Content Based Image
Retrievals (CBIR) to retrieve query image or sub region of image to find similar images in
image database. To improve query result, relevance feedback is used many times in CBIR to
help user to express their preference and improve query results. In this paper, a new approach
for image retrieval is proposed which is based on the features such as Color Histogram, Eigen
Values and Match Point. Images from various types of database are first identified by using
edge detection techniques .Once the image is identified, then the image is searched in the
particular database, then all related images are displayed. This will save the retrieval time.
Further to retrieve the precise query image, any of the three techniques are used and
comparison is done w.r.t. average retrieval time. Eigen value technique found to be the best as
compared with other two techniques.
The development of multimedia system technology in Content based Image Retrieval (CBIR) System is
one in every of the outstanding area to retrieve the images from an oversized collection of database. The feature
vectors of the query image are compared with feature vectors of the database images to get matching images.It is
much observed that anyone algorithm isn't beneficial in extracting all differing kinds of natural images. Thus an
intensive analysis of certain color, texture and shape extraction techniques are allotted to spot an efficient CBIR
technique that suits for a selected sort of images. The Extraction of an image includes feature description and
feature extraction. During this paper, we tend to projected Color Layout Descriptor (CLD), grey Level Co-
Occurrences Matrix (GLCM), Marker-Controlled Watershed Segmentation feature extraction technique that
extract the matching image based on the similarity of Color, Texture and shape within the database. For
performance analysis, the image retrieval timing results of the projected technique is calculated and compared
with every of the individual feature.
Face skin color based recognition using local spectral and gray scale featureseSAT Journals
Abstract Human face conveys more information about identity of person. Human face recognition is one of the most challenging problem and it can be used in many applications at different security places in airports, defense and banking sectors etc.In this work used colored features obtained from color segmentation because in real time scenario color provides the more information than gray scale image but it has a drawback. To overcome this drawback gray scale feature extracted from co-occurrence matrix of an image and for efficient face recognition of human Face under different illumination conditions spectral features can be extracted from face texture. These three feature vectors concatenated into a single feature vector and applied Lenc-Kral matching technique to measure similarity between the database and query image, the similarity is high then face is recognized. Keywords: Face recognition, illumination condition, local texture features, color segmentation.
PDE BASED FEATURES FOR TEXTURE ANALYSIS USING WAVELET TRANSFORMIJCI JOURNAL
In the present paper, a novel method of partial differential equation (PDE) based features for texture
analysis using wavelet transform is proposed. The aim of the proposed method is to investigate texture
descriptors that perform better with low computational cost. Wavelet transform is applied to obtain
directional information from the image. Anisotropic diffusion is used to find texture approximation from
directional information. Further, texture approximation is used to compute various statistical features.
LDA is employed to enhance the class separability. The k-NN classifier with tenfold experimentation is
used for classification. The proposed method is evaluated on Brodatz dataset. The experimental results
demonstrate the effectiveness of the method as compared to the other methods in the literature.
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
A binarization technique for extraction of devanagari text from camera based ...sipij
This paper presents a binarization method for camera based natural scene (NS) images based on edge
analysis and morphological dilation. Image is converted to grey scale image and edge detection is carried
out using canny edge detection. The edge image is dilated using morphological dilation and analyzed to
remove edges corresponding to non-text regions. The image is binarized using mean and standard
deviation of edge pixels. Post processing of resulting images is done to fill gaps and to smooth text strokes.
The algorithm is tested on a variety of NS images captured using a digital camera under variable
resolutions, lightening conditions having text of different fonts, styles and backgrounds. The results are
compared with other standard techniques. The method is fast and works well for camera based natural
scene images.
An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance FeedbackIJMIT JOURNAL
Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database. Image contents are plays significant role for image retrieval. There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design. Each image has three contents such as: color, texture and shape features. Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results. Color histogram and texture features have potential to retrieve similar images on the basis of their properties. As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query. To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solutio
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.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Noise tolerant color image segmentation using support vector machineeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Query Image Searching With Integrated Textual and Visual Relevance Feedback f...IJERA Editor
There are many researchers who have studied the relevance feedback in the literature of content based image
retrieval (CBIR) community, but none of CBIR search engines support it because of scalability, effectiveness
and efficiency issues. In this, we had implemented an integrated relevance feedback for retrieving of web
images. Here, we had concentrated on integration of both textual features (TF) and visual features (VF) based
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Image Information Retrieval From Incomplete Queries Using Color and Shape Features
1. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.4, December 2011
DOI : 10.5121/sipij.2011.2418 213
IMAGE INFORMATION RETRIEVAL FROM
INCOMPLETE QUERIES USING COLOR AND SHAPE
FEATURES
Bikesh Kumar Singh1
, A. S. Thoke2
, Keshri Verma3
and Ankita Chandrakar4
1
Department of Biomedical Engineering, N. I.T Raipur, C.G (India)
bsingh.bme@nitrr.ac.in
2
Department of Electrical Engineering, N. I.T Raipur, C.G (India)
asthoke@yahoo.co.in
3
Department of M.C.A, N. I.T Raipur, C.G (India)
keshriverma@gmail.com
4
Department of Electrical Engineering, N. I.T Raipur, C.G (India)
ankitachandrakar007@gmail.com
ABSTRACT
Content based image retrieval (CBIR) is the task of searching digital images from a large database based
on the extraction of features, such as color, texture and shape of the image. Most of the research in CBIR
has been carried out with complete queries which were present in the database. This paper investigates
utility of CBIR techniques for retrieval of incomplete and distorted queries. Studies were made in two
categories of the query: first is complete and second is incomplete. The query image is considered to be
distorted or incomplete image if it has some missing information, some undesirable objects, blurring, noise
due to disturbance at the time of image acquisition etc. Color (hue, saturation and value (HSV) color space
model) and shape (moment invariants and Fourier descriptor) features are used to represent the image.
The algorithm was tested on database consisting of 1875 images. The results show that retrieval accuracy
of incomplete queries is highly increased by fusing color and shape features giving precision of 79.87%.
MATLAB ® 7.01 and its image processing toolbox have been used to implement the algorithm.
KEYWORDS
Content based image retrieval, color image, incomplete query image, color feature, shape feature.
1. INTRODUCTION
Content based image retrieval CBIR systems uses the contents of a query image provided by the
user to search similar images in a very large database. The most common methods used are based
on color, shape, texture information or combinations of these [1]. Color, texture and shape are the
most important visual features of image. Color feature plays an important role in CBIR due to its
robustness to complex background and independent of image size and orientation. However,
using color alone is insufficient to distinguish images because some images have the same color
proportions but different spatial distributions. This results in CBIR scheme which combine
multiple features in order to achieve better retrieval performance [2, 3]. HSV color space is
widely used in computer graphics, visualization in scientific computing and other fields [4, 5].
Therefore, the proposed algorithm selects the HSV color space to extract the color features such
as hue, saturation and value. An effective shape feature is a key component, since shape is a
basic property of an object present in the image itself. Most of existing shape descriptors is
usually either application dependent or non-robust, making them undesirable for shape
2. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.4, December 2011
214
description [6, 7]. In this paper we investigate and analyze color and shape feature to retrieve
incomplete query from database consisting of large number of similar images. The proposed
system results the images from the database which are most similar to input incomplete query
image. The feature vector of the query image is compared with the feature vector of each image
in the database image using Euclidean distance measure.
2. OVERVIEW OF FEATURE EXTRACTION
Generally, any CBIR techniques use visual features of images, such as color, shape and texture
yielding vectors with hundreds or even thousands of features. As many features are correlated to
others it brings extra knowledge and can deteriorate the ability of the system to correctly
distinguish them. Moreover, having a large number of features leads to the “dimensionality curse”
problem, where the indexing, retrieval and comparing techniques collapse, due to the fact that it is
not possible to well separate the data, making the process of storing, indexing and retrieving
extremely time consuming. Thus, the key challenges of searching similar images from a large
database are the high computational overhead due to the “dimensionality curse” and the semantic
gap [8, 9]. In this paper we use color and shape features. Collectively all these features were
combining to form a single vector which we call as feature vector.
2.1. Shape Analysis
An effective, working and efficient shape descriptor is a key component of content description for
an image, since shape is a basic property of an object present in the image itself [10]. These
shape descriptors are broadly categorized into two groups, i.e., contour-based shape descriptors
and region based shape descriptors. Due to the fact that contour-based shape descriptors exploit
only boundary information, they cannot capture the interior shape of the objects and also these
methods cannot deal with disjoint shapes or the shape which is not closed where contour
information is not available. In region based techniques, shape descriptors are derived using all
the pixel information within a shape region. Region-based shape descriptors can be applied to
general applications [6]. Fourier Descriptor (FD) and Moment Invariants are used for shape
analysis since they are perfect shape descriptor and do not produce redundant values. Since the
colored image has several kinds of objects, Fourier descriptor may be the best possible descriptor
for calculating the boundaries of the objects present in the images. The proposed shape descriptor
is derived by applying 2-D Fourier transform on an image. The acquired shape descriptor is
application independent and robust. Their main advantages are that they are invariant to
translation, rotation and scaling of the observed object. Thus shape description become
independent of the relative position and size of the object in the input image [11, 12].
In order to determine Fourier descriptors first the image is converted into the binary image and
then filtered using a Gaussian mask of size 15x15 with variance, σ = 9 and threshold of 0.7.Then
the image is segmented and boundary of the object is determined. The boundary is presented as
an array of complex numbers which correspond to the pixels of the object boundary if the image
is placed in the complex plane. Fourier descriptors are now calculated by combining Fourier
transform coefficients of the complex array. Let the complex array z0, z1, z2 … zN-1 represents the
boundary belonging to the object whose shape needs to be described. The k-th Fourier transform
Coefficient is calculated as [13].
N
k
N
n
n
n
in
e
Z
Z
π
2
1
0
−
−
=
∑
= Where k= 0, 1, 2,…….., N-1 (1)
3. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.4, December 2011
215
The Fourier descriptors are obtained from the sequence Zk by truncating elements z1 and z2, then
by taking the absolute value of the remaining elements and dividing every element obtained array
by z . To summarize, the Fourier descriptors are:
, (2)
For N-point digital boundary in the xy-plane, starting at an arbitrary point (x0, y0), coordinate
pairs (x0, y0), (x1, y1) …, (xN-1, yN-1) are encountered in traversing the boundary, say, in the
counterclockwise direction. These coordinates can be expressed in the form x (n) = xn and y(n)
=yn. With this notation, the boundary itself can be represented as the sequence of coordinates
s(n)=[x(n),y(n)], for n = 0, 1, 2,……., N-1. Moreover, each coordinate pair can be treated as a
complex number so that
(3)
The Discrete Fourier Transform (DFT) of s (n) is
∑
−
=
−
=
1
0
2
)
(
)
(
N
n
N
jun
e
n
s
u
a ,u=0, 1, 2, ……………N-1 (4)
The complex coefficients a(u) are called the Fourier descriptors of the boundary. The inverse
Fourier transform of these coefficients restores s (n). That is,
∑
−
=
=
1
0
2
)
(
1
)
(
N
u
N
jun
e
u
a
N
n
s
π
,n=0, 1, 2, ................N-1 (5)
Suppose, however, that instead of all the Fourier coefficients, only the first P coefficients are
used. This is equivalent to setting a(u) = 0 for u > P-1 in the preceding equation for a(u). This
result is the following approximation to s (n):
N
jun
p
u
e
u
a
p
n
s
π
2
1
0
)
(
1
)
( ∑
−
=
= , n= 0, 1, 2...............N-1 (6)
Although only P terms are used to obtain each component of ŝ (n), n still ranges from 0 to K-1.
That is, the same number of points exists in the approximate boundary, but not so many terms are
used in the reconstruction of each point. The high-frequency components account for fine detail,
and low-frequency components determine global shape. Thus, loss of detail in the boundary
increases as P decreases.Although only P terms are used to obtain each component of ŝ (n), n still
ranges from 0 to K-1. That is, the same number of points exists in the approximate boundary, but
not so many terms are used in the reconstruction of each point. The high-frequency components
account for fine detail, and low-frequency components determine global shape. Thus, loss of
detail in the boundary increases as P decreases.
The second feature which is used for shape description is Moment Invariants. Regular moment
invariants are one of the most popular and widely used contour-based shape descriptors is a set of
features derived by Hu (1962). Hu’s moment invariants and extended Zernike moments were
used as feature extractors.
A two-dimensional moment of a digitally sampled M × M image that has gray function
is given as [14]:
4. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.4, December 2011
216
The moments f (x, y) translated by an amount (a, b), are defined as
(8)
Thus the central moments or can be computed from (2) on substituting a = −x and b = −y
as,
, (9)
(10)
When a scaling normalization is applied the central moments change as,
(11)
(12)
In particular, Hu (1962) defines seven values, computed by normalizing central moments through
order three, that are invariant to object scale, position, and orientation.
2.2. Color Analysis
We use HSV color model for extracting color feature such as hue, saturation and value which is a
type of color space. In HSV, hue represents color, Saturation indicates the range of grey in the
color space and Value is the brightness of the color and varies with color saturation. The
advantage of using HSV color space is that it selects various different colors needed from a
particular image. In general it gives the color according to human perception [15].
3. PROPOSED METHODOLOGY
3.1. Development Environment
The functional code for our prototype system was implemented using MATLAB ® 7.0 on a
Pentium dual core II, 2.48 GHz Windows-based PC.
3.2. Database Preparation
We use 1875 different digital color images of various categories collected from the open source
[16]. Candidate image terminology is used for the image which is already stored in the database.
Query image terminology is used for the image given to the CBIR system and it is incomplete
image. We use two databases one for images and another for feature vector of these images.
Feature vector of the incomplete query image is calculated at the run time.
3.3. Determining Feature Vector
Our database contains different objects like flowers, animals, monuments etc. The color and
shape features discussed in section 2 were extracted and represented as a single array which we
call as feature vector.
5. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.4, December 2011
217
3.5 Distance Calculation
The efficiency and accuracy of the image retrieval is significantly affected by the ability of the
distance calculation techniques. Let be the feature vector of the
candidate image and be the feature vector of the incomplete query
image. Euclidean distance between the candidate image feature vector and the query image
feature vector is given by [17]. The result of the distance calculation is used for retrieving images
similar to incomplete query image.
∑
=
−
=
n
i
i
i Y
X
D
1
2
1
2
)
( (13)
The result of the distance calculation is used for retrieving images similar to query image. Figure
1 shows the block diagram of proposed method.
Figure 1. Block Diagram of Proposed Method
4. RESULTS AND CONCLUSIONS
Figure 2 to figure 5 shows the results of proposed methodology. It is verified from the figure 2
that when only shape feature is used for retrieval of query image the correct image is at sixth
position, thus giving poor retrieval accuracy. Thus here we conclude that shape features are
highly affected if query is incomplete and may result in very low retrieval efficiency. Thus to
increase the retrieval efficiency of the proposed system we combine both color and shape
features. We give more weightage to the color feature vector in the hybrid approach. Fig 3 to 5
shows retrieval results using both color and shape feature. When both color and shape features
were combined it is observed from figure 3-5, that the query image is retrieved at first position
thus increasing the retrieval accuracy.
For the performance evaluation of retrieval system, Precision calculation is used. Two class
precision is calculated by categorizing the database into two classes. One class is Animal and
6. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.4, December 2011
218
other is Non-animal. The category of animal includes dolphin, tiger, fish , starfish , penguin ,
cougar , ant , butterfly ,etc and in the category of Non-animal there are sunflower , water lily ,
tree , fruits , etc. Precision is mathematically defined as:
Precision =
k
r
f
t
t
p
p
p
=
+
(14)
tp is the number of images retrieved relevant to the query, fp is the number of images retrieved
irrelevant to the query. In this result tp is the number of animal images and fp is the number of non
– animal images, where r is the number of total number of relevant image retrieved and k is the
total number of images retrieved. The incomplete/ distorted query is presented to the CBIR
system and their precision is calculated. The precision is calculated for shape features, color
features and their combined approach. The precision is calculated for the average 100 incomplete
queries given to the system. Table 1 shows the results.
Figure 2. Query image (left) and similar images (right) retrieved by the system using only shape
feature.
Figure 3. Query image (left) and similar images (right) retrieved by the system using shape and color
feature.
Figure 4. Query image (left) and similar images (right) retrieved by the system using shape and color
feature.
7. Signal & Image Processing : An International Journal (SIPIJ) Vol.2, No.4, December 2011
219
Figure 5. Query image (left) and similar images (right) retrieved by the system using shape and color
feature.
Table 1. Precision calculation using color and shape features
S.No Feature Precision (%)
1 Shape (Moment invariant, Fourier descriptor) 68.42
2 Color (HSV color space model) 75.87
3 Combined Approach 79.87
5. CONCLUSIONS
In this research retrieval of incomplete/distorted queries using shape and color analysis is
addressed. Experiments were conducted on 1875 color images collected from open source
database. It is found that if shape features alone were used for retrieval of incomplete/distorted
queries the retrieval accuracy is very poor giving precision of 68.42%. Thus shape feature is not
sufficient if the query is incomplete or distorted. To address this problem we combine color
features with shape features to retrieve query image from the database. The result shows that
retrieval accuracy is highly increased by fusing color and shape features giving precision of
79.87%. The performance of the proposed system can be further improved by including other
CBIR features such as textures features.
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