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.
Wavelet-Based Color Histogram on Content-Based Image RetrievalTELKOMNIKA JOURNAL
The growth of image databases in many domains, including fashion, biometric, graphic design,
architecture, etc. has increased rapidly. Content Based Image Retrieval System (CBIR) is a technique used
for finding relevant images from those huge and unannotated image databases based on low-level features
of the query images. In this study, an attempt to employ 2nd level Wavelet Based Color Histogram (WBCH)
on a CBIR system is proposed. Image database used in this study are taken from Wang’s image database
containing 1000 color images. The experiment results show that 2nd level WBCH gives better precision
(0.777) than the other methods, including 1st level WBCH, Color Histogram, Color Co-occurrence Matrix,
and Wavelet texture feature. It can be concluded that the 2nd Level of WBCH can be applied to CBIR system.
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.
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.
Wavelet-Based Color Histogram on Content-Based Image RetrievalTELKOMNIKA JOURNAL
The growth of image databases in many domains, including fashion, biometric, graphic design,
architecture, etc. has increased rapidly. Content Based Image Retrieval System (CBIR) is a technique used
for finding relevant images from those huge and unannotated image databases based on low-level features
of the query images. In this study, an attempt to employ 2nd level Wavelet Based Color Histogram (WBCH)
on a CBIR system is proposed. Image database used in this study are taken from Wang’s image database
containing 1000 color images. The experiment results show that 2nd level WBCH gives better precision
(0.777) than the other methods, including 1st level WBCH, Color Histogram, Color Co-occurrence Matrix,
and Wavelet texture feature. It can be concluded that the 2nd Level of WBCH can be applied to CBIR system.
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.
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.
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.
Automated Colorization of Grayscale Images Using Texture DescriptorsIDES Editor
A novel example-based process for automated
colorization of grayscale images using texture descriptors
(ACTD) without any human intervention is proposed. By
analyzing a set of sample color images, coherent regions of
homogeneous textures are extracted. A multi-channel filtering
technique is used for texture-based image segmentation. For
each area of interest, state of the art texture descriptors are
then computed and stored, along with corresponding color
information. These texture descriptors and the color
information are used for colorization of a grayscale image with
similar textures. Given a grayscale image to be colorized, the
segmentation and feature extraction processes are repeated.
The texture descriptors are used to perform Content-Based
Image Retrieval (CBIR). The colorization process is performed
by chroma replacement. This research finds numerous
applications, ranging from classic film restoration and
enhancement, to adding valuable information into medical and
satellite imaging, and to enhance the detection of objects from
x-ray images at the airports.
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.
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.
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
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).
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
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.
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.
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
MMFO: modified moth flame optimization algorithm for region based RGB color i...IJECEIAES
Region-based color image segmentation is elementary steps in image processing and computer vision. The region-based color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, in which three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper, L*a*b color space conversion has been used to reduce the one dimension and geometrically it converts in the array hence the further one dimension has been reduced. This paper introduced, an improved algorithm modified moth flame optimization (MMFO) algorithm for RGB color image segmentation which is based on bio-inspired techniques. The simulation results of MMFO for region based color image segmentation are performed better as compared to PSO and GA, in terms of computation times for all the images. The experiment results of this method gives clear segments based on the different color and the different number of clusters is used during the segmentation process.
Content Based Image Retrieval : Classification Using Neural Networksijma
In a content-based image retrieval system (CBIR), the main issue is to extract the image features that
effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of
retrieval performance of image features. This paper presents a review of fundamental aspects of content
based image retrieval including feature extraction of color and texture features. Commonly used color
features including color moments, color histogram and color correlogram and Gabor texture are
compared. The paper reviews the increase in efficiency of image retrieval when the color and texture
features are combined. The similarity measures based on which matches are made and images are
retrieved are also discussed. For effective indexing and fast searching of images based on visual features,
neural network based pattern learning can be used to achieve effective classification.
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
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.
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.
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.
Automated Colorization of Grayscale Images Using Texture DescriptorsIDES Editor
A novel example-based process for automated
colorization of grayscale images using texture descriptors
(ACTD) without any human intervention is proposed. By
analyzing a set of sample color images, coherent regions of
homogeneous textures are extracted. A multi-channel filtering
technique is used for texture-based image segmentation. For
each area of interest, state of the art texture descriptors are
then computed and stored, along with corresponding color
information. These texture descriptors and the color
information are used for colorization of a grayscale image with
similar textures. Given a grayscale image to be colorized, the
segmentation and feature extraction processes are repeated.
The texture descriptors are used to perform Content-Based
Image Retrieval (CBIR). The colorization process is performed
by chroma replacement. This research finds numerous
applications, ranging from classic film restoration and
enhancement, to adding valuable information into medical and
satellite imaging, and to enhance the detection of objects from
x-ray images at the airports.
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.
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.
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
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).
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
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.
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.
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
MMFO: modified moth flame optimization algorithm for region based RGB color i...IJECEIAES
Region-based color image segmentation is elementary steps in image processing and computer vision. The region-based color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, in which three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper, L*a*b color space conversion has been used to reduce the one dimension and geometrically it converts in the array hence the further one dimension has been reduced. This paper introduced, an improved algorithm modified moth flame optimization (MMFO) algorithm for RGB color image segmentation which is based on bio-inspired techniques. The simulation results of MMFO for region based color image segmentation are performed better as compared to PSO and GA, in terms of computation times for all the images. The experiment results of this method gives clear segments based on the different color and the different number of clusters is used during the segmentation process.
Content Based Image Retrieval : Classification Using Neural Networksijma
In a content-based image retrieval system (CBIR), the main issue is to extract the image features that
effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of
retrieval performance of image features. This paper presents a review of fundamental aspects of content
based image retrieval including feature extraction of color and texture features. Commonly used color
features including color moments, color histogram and color correlogram and Gabor texture are
compared. The paper reviews the increase in efficiency of image retrieval when the color and texture
features are combined. The similarity measures based on which matches are made and images are
retrieved are also discussed. For effective indexing and fast searching of images based on visual features,
neural network based pattern learning can be used to achieve effective classification.
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
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.
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.
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.
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.
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.
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.
My books- Hacking Digital Learning Strategies http://hackingdls.com & Learning to Go https://gum.co/learn2go
Resources at http://shellyterrell.com/classmanagement
The reality for companies that are trying to figure out their blogging or content strategy is that there's a lot of content to write beyond just the "buy now" page.
Content Based Image Retrieval Using Dominant Color and Texture FeaturesIJMTST Journal
The purpose of this Paper is to describe our research on different feature extraction and matching techniques in designing a Content Based Image Retrieval (CBIR) system. Due to the enormous increase in image database sizes, as well as its vast deployment in various applications, the need for CBIR development arose. Content Based Image Retrieval (CBIR) is the retrieval of images based on features such as color and texture. Image retrieval using color feature cannot provide good solution for accuracy and efficiency. The most important features are Color and texture. In this paper technique used for retrieving the images based on their content namely dominant color, texture and combination of both color and texture. The technique verifies the superiority of image retrieval using multi feature than the single feature.
Content Based Image Retrieval (CBIR) is one of the
most active in the current research field of multimedia retrieval.
It retrieves the images from the large databases based on images
feature like color, texture and shape. In this paper, Image
retrieval based on multi feature fusion is achieved by color and
texture features as well as the similarity measures are
investigated. The work of color feature extraction is obtained by
using Quadratic Distance and texture features by using Pyramid
Structure Wavelet Transforms and Gray level co-occurrence
matrix. We are comparing all these methods for best image
retrieval
Engine explained in this ppt ,takes a query image as an input do some process on it ,compare this image with images present in database and retrieve similar images. It uses the concept of content based image retrieval.
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.
Low level features for image retrieval basedcaijjournal
In this paper, we present a novel approach for image retrieval based on extraction of low level features
using techniques such as Directional Binary Code (DBC), Haar Wavelet transform and Histogram of
Oriented Gradients (HOG). The DBC texture descriptor captures the spatial relationship between any pair
of neighbourhood pixels in a local region along a given direction, while Local Binary Patterns (LBP)
descriptor considers the relationship between a given pixel and its surrounding neighbours. Therefore,
DBC captures more spatial information than LBP and its variants, also it can extract more edge
information than LBP. Hence, we employ DBC technique in order to extract grey level texture features
(texture map) from each RGB channels individually and computed texture maps are further combined
which represents colour texture features (colour texture map) of an image. Then, we decomposed the
extracted colour texture map and original image using Haar wavelet transform. Finally, we encode the
shape and local features of wavelet transformed images using Histogram of Oriented Gradients (HOG) for
content based image retrieval. The performance of proposed method is compared with existing methods on
two databases such as Wang’s corel image and Caltech 256. The evaluation results show that our
approach outperforms the existing methods for image retrieval.
Invention of digital technology has lead to increase in the number of images that can be stored in digital format. So searching and retrieving images in large image databases has become more challenging. From the last few years, Content Based Image Retrieval (CBIR) gained increasing attention from researcher. CBIR is a system which uses visual features of image to search user required image from large image
database and user’s requests in the form of a query image. Important features of images are colour, texture and shape which give detailed information about the image. CBIR techniques using different feature extraction techniques are discussed in this paper.
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.
A hybrid content based image retrieval system using log-gabor filter banksIJECEIAES
In this paper, a new efficient image retrieval system using sequential process of three stages with filtering technique for the feature selection is proposed. In the first stage the color features are extracted using color histogram method and in the second stage the texture features are obtained using log-Gabor filters and in the third stage shape features are extracted using shape descriptors using polygonal fitting algorithm. The proposed log-Gabor filter in the second stage has advantages of retrieving images over regular Gabor filter for texture. It provides better representation of the images. Experimental evaluation of the proposed system shows improved performance in retrieval as compared to other existing systems in terms of average precision and average recall.
In Content-Based Image Retrieval (CBIR) systems, the visual contents of the
images in the database are took out and represented by multi-dimensional characteristic
vectors. A well known CBIR system that retrieves images by unsupervised method known
as cluster based image retrieval system. For enhancing the performance and retrieval rate
of CBIR system, we fuse the visual contents of an image. Recently, we developed two
cluster-based CBIR systems by fusing the scores of two visual contents of an image. In this
paper, we analyzed the performance of the two recommended CBIR systems at different
levels of precision using images of varying sizes and resolutions. We also compared the
performance of the recommended systems with that of the other two existing CBIR systems
namely UFM and CLUE. Experimentally, we find that the recommended systems
outperform the other two existing systems and one recommended system also comparatively
performed better in every resolution of image.
SIGNIFICANCE OF DIMENSIONALITY REDUCTION IN IMAGE PROCESSING sipij
The aim of this paper is to present a comparative study of two linear dimension reduction methods namely
PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The main idea of PCA is to
transform the high dimensional input space onto the feature space where the maximal variance is
displayed. The feature selection in traditional LDA is obtained by maximizing the difference between
classes and minimizing the distance within classes. PCA finds the axes with maximum variance for the
whole data set where LDA tries to find the axes for best class seperability. The neural network is trained
about the reduced feature set (using PCA or LDA) of images in the database for fast searching of images
from the database using back propagation algorithm. The proposed method is experimented over a general
image database using Matlab. The performance of these systems has been evaluated by Precision and
Recall measures. Experimental results show that PCA gives the better performance in terms of higher
precision and recall values with lesser computational complexity than LDA
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.
Similar to Research Inventy : International Journal of Engineering and Science (20)
Research Inventy : International Journal of Engineering and Science
1. Research Inventy: International Journal Of Engineering And Science
ISBN: 2319-6483, Issn: 2278-4721, Vol. 1, Issue 8 (November 2012), Pp 18-24
Www.Researchinventy.Com
Feature Extraction for Image Retrieval using Image Mining
Techniques
1
Madhubala Myneni, 2Dr.M.Seetha
1,
Professor In Dept. CSE, AARW, Hydearabad
2,
Professor In Dept. CSE, G.N.I.T. S, Hyderabad
Abstract: In this paper feature extraction process is analyzed and a new set of integrated features are proposed
for i mage retrieval. In i mage retrieval system, the content of an image can be expressed in terms of different
features as color, texture and shape. This paper emphasizes on feature extraction algorithms and performance
comparison among all algorithms and image mining techniques for converting low level semantic characteristics
into high level features. The primitive features are extracted and compared with data set by using various feature
extraction algorithms like color histograms, wavelet decomposition and canny algorithms. In this paper feature
integration has restricted to five different methodologies of feature Integration: shape only, color only, texture only,
color and texture only, and shape, color and texture. It is ascertained that the performance is superior when the
image retrieval based on the integrated features, and better results than primitive set.
Key Words: Feature Extraction, Feature Integration, Image Retrieval, Image Mining
I. Introduction
Image Retrieval aims to provide an effective and efficient tool for managing large image databases.
Image retrieval and searching is one of the most exciting and fastest growing research areas in the field of d igital
imaging [2]. The goal of CBIR is to retrieve images fro m a database that are similar to an image placed as a query.
In CBIR, for each image in the database, features are extracted and compared to the features of the query image. A
CBIR method typically converts an image into a feature vector representation and matches with the images in the
database to find out the most similar images. In various studies different databases have been to compare the study.
Content-Based Image Retrieval (CBIR) systems index images using their v isual characteristics, such as color,
texture and shape, which can be extracted fro m image itself automat ically. The similarity between features was to
be calculated using algorithms used by well known CBIR systems such as IBM's QBIC. For each specific feature
there is a specific algorith m for extraction and another for matching.
The integration of structure features, which are part icularly suitable for the retrieval of man made
objects, and color and texture features, which are geared towards the retrieval of natural i mages in general.
Specifically, the attention was restricted to three different methodologies of feature integration: color and texture,
color and shape. Texture and shape results in better performance than using shape, color, and texture individually.
II. Feature Extraction
Feature Extract ion is the process of creating a representation, or a transformation fro m the original data.
The images have the primit ive features like colo r, texture, shape, edge, shadows, temporal details etc. The features
that were most promising were co lor, texture and shape/edge. The reasons are color can occur in limited range of
set. Hence the picture elements can be compared to these spectra. Texture is defined as a neighbourhood feature as
a region or a block. The variation of each pixel with respect to its neighbouring pixels defines texture. Hence the
textural details of similar reg ions can be compared with a texture template. shape/edge is simply a large change in
frequency. The three feature descriptors mainly used most frequently during feature ext raction are co lor, texture
and shape.
The main method of representing color informat ion of images in Image Retrieval Systems is through
color histograms. Quantization in terms of co lor histograms refers to the process of reducing the n umber of bins by
taking colors that are very similar to each other and putting them in the same bin. There are t wo types of color
histograms, Global color h istograms (GCHs) and Local color h istograms (LCHs). A GCH represents one whole
image with a single color h istogram. An LCH div ides an image into fixed blocks and takes the color histogram of
each of those blocks. LCHs contain more informat ion about an image but are computationally expensive when
comparing images. “The GCH is the trad itional method for co lor based image retrieval. Ho wever, it does not
include informat ion concerning the color d istribution of the regions” of an image. Thus when co mparing GCHs
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2. Feature Extraction For Image Retrieval Using Image Mining...
one might not always get a proper result in terms of similarity of images.Texture feature descriptors, extracted
through the use of statistical methods, can be classified into two categories according to the order of the statistical
function that is utilized: First-Order Texture Features and Second Order Texture Features. First Order Texture
Features are extracted exclusively fro m the informat ion provided by the intensity histograms, thus yield no
informat ion about the locations of the pixels. Another term used for First -Order Textu re Features is Grey Level
Distribution Mo ments. In contrast, Second-Order Texture Features take the specific position of a p ixel relat ive to
another into account. The most popularly used of second -order methods is the Spatial Grey Level Dependency
Matrix (SGLDM) method. The method roughly consists of constructing matrices by cou nting the number of
occurrences of pixel pairs of g iven intensities at a given displacement.
Shape may be defined as the characteristic surface configuration of an object; an outline or contour.
Canny edge detection is an optimal smoothing filter given the criteria of detection, localization and minimizing
mu ltip le responses to a single edge. This method showed that the optimal filter given these assumptions is a sum
of four exponential terms. It also showed that this filter can be well appro ximated by first-order derivatives of
Gaussians. Canny also introduced the notion of non-maximu m suppression, which means that given the
pre-smoothing filters, edge points are defined as points where the gradient magnitude assumes a local maximu m
in the gradient direction. Sobel edge detection operations are performed on the data and the processed data is sent
back to the computer. The transfer of data is done using parallel port interface operating in bidirectional mode. For
estimating image gradients fro m the input image or a smoothed version of it, d ifferent gradient operators can be
applied. The simplest approach is to use central differences:
corresponding to the application of the follo wing filter masks to the image data:
The well-known and earlier Sobel operator is based on the following filters:
Given such estimates of first- order derivatives, the gradient magnitude is then computed as:
while the gradient orientation can be estimated as
III. Image Retrieval
In general all Image Retrieval algorith ms are based on image primit ive features like color, texture and
shape. In this paper, the proposal of combinational features is specified to give good performance. On each feature
more efficient algorith ms are used to retrieve the information fro m data set.
3.1. Image Retrieval based on Color
In this paper color based image retrieval has performed in two steps. First for ext racting color feature
informat ion global color histograms has used. To quantize the colors, number of bins are 20. Second step is
calculating the distance between bins by using quadratic distance algorithm. The results are the distance from zero
the less similar the images are in co lor similarity.
3.1.1 Col or Histograms
Global co lor histograms extract the color features of images. To quantize the number of b ins are 20. This
means that colors that are distinct yet similar are assigned to the same bin reducing the number of bins from 256 to
20. This obviously decreases the information content of images, but decreases the time in calcu lating the color
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3. Feature Extraction For Image Retrieval Using Image Mining...
distance between two histograms. On the other hand keeping the number of b ins at 256 g ives a mo re accurate
result in terms of color distance. Later on we went back to 256 bins due to some inconsistencies obtained in the
color distances between images. There hasn't been any evidence to show which co lor s pace generates the best
retrieval results, thus the use of this color space did not restrict us an anyway.
3.1.2 Quadratic Distance Algorithm
The equation we used in deriving the distance between two color histograms is the quadratic distance
metric:
d 2 Q, I HQ HI A H
t
Q HI
The equation consists of three terms. The derivation of each of these terms will be explained in the following
sections. The first term consists of the difference between two co lor histograms; or mo re precisely the difference
in the number of pixels in each bin. This term is obviously a vector since it consists of one row. The number of
columns in this vector is the number of bins in a histogram. The third term is the transpose of that vector. The
middle term is the similarity matrix. The final result d represents the color distance between two images. The
closer the distance is to zero the closer the images are in color similarity. The further the distance fro m zero the
less similar the images are in color similarity.
3.1.3 Similarity Matrix
As can be seen from the color h istograms of two images Q and I, the color patterns observed in the color
bar are totally different. A simple d istance metric involving the subtraction of the number of pixels in the 1 st bin of
one histogram fro m the 1st bin of another histogram and so on is not adequate. This metric is referred to as a
minko wski-form distance metric, wh ich only co mpares the “same bins between color histograms “.
This is the main reason for using the quadratic distance metric. More p recisely it is the middle term of the equation
or similarity matrix A that helps us overcome the problem of different color maps. The similarity mat rix is
obtained through a complex algorithm:
1
sq cos hq si coshi s
sin hq si sinhi 2
2 2 2
vq vi
q
aq ,i 1
5
which basically co mpares one color bin of HQ with all those of HI to try and find out which color bin is the most
similar.
3.2 Image Retrieval based on Texture
The texture based image retrieval was performed in three steps. First fo r ext racting statistical texture
informat ion Pyramid-structured wavelet t ransform has used. This decomposition has been done in five levels.
Second step is calculating energy levels on each decomposition level. Third step is calculat ing euclid ian distance
between query image and database images. The top most five images fro m the list are displayed as query result.
3.2.1. Pyrami d-Structured Wavelet Transform
This transformation technique is suitable for signals consisting of components with informat ion
concentrated in lower frequency channels. Due to the innate image properties that allows for most information to
exist in lower sub-bands, the pyramid -structured wavelet transform is highly sufficient. Using the
pyramid-structured wavelet transform, the texture image is decomposed into four sub images, in low -lo w,
low-h igh, high-lo w and high-high sub-bands. At this point, the energy level of each sub-band is calculated .This is
first level decomposition. Using the low-low sub-band for further decomposition, we reached fifth level
decomposition, for our project. The reason for this is the basic assumption that the energy of an image is
concentrated in the low-low band. For this reason the wavelet function used is the daubechies wavelet.
For this reason, it is mostly suitable for signals consisting of components with informat ion concentrated in lower
frequency channels. Due to the innate image properties that allows for most informat ion to exist in lower
sub-bands, the pyramid-structured wavelet transform is highly sufficient.
3.2.2 Energy Level
Energy Level Algorith m:
Deco mpose the image into four sub-images
Calculate the energy of all decomposed images at the same scale, using [2]:
20
4. Feature Extraction For Image Retrieval Using Image Mining...
m n
X i , j
1
E
MN
i 1 j 1
where M and N are the dimensions of the image, and X is the intensity of the pixel located at row i and column j in
the image map.
Repeat fro m step 1 for the low-low sub-band image, until index ind is equal to 5. Increment ind.
Using the above algorith m, the energy levels of the sub-bands were calculated, and further deco mposition of the
low-low sub-band image. This is repeated five t imes, to reach fifth level deco mposition. These energy level values
are stored to be used in the Euclidean distance algorith m.
3.2.3 Euclidean Distance
euclidean distance algorithm:
- Deco mpose query image.
- Get the energies of the first dominant k channels.
- For image i in the database obtain the k energies.
- Calculate the euclidean distance between the two sets of energies, using [2]:
x
k
2
Di k yi , k
k 1
- Increment i. Repeat fro m step 3.
Using the above algorithm, the query image is searched for in the image database. The euclidean distance is
calculated between the query image and every image in the database. This process is repeated until all the images
in the database have been compared with the query image. Upon complet ion of the euclidean distance algorith m,
we have an array of euclidean distances, which is then sorted. The five topmost images are then displayed as a
result of the texture search.
3.3 Image Retrieval based on Shape
In this paper shape based image retrieval has performed in two steps. First for extracting edge feature
canny edge detection algorithm and sobel edge detection algorithm are us ed. Second step is calculating euclid ian
distance between query image and database images. The top most five images from the list are displayed as query
result.
3.3.1. Canny Edge Detection Algorithm
The canny edge detection algorith m was used to detect a wide range of edges in images. The stages of the
canny algorithm include the noise reduction, finding the intensity gradient of the image, non-maximu m
suppression, tracing edges through the image and hysteresis threshold, differentia l geomet ric formulat ion of the
canny edge detector
IV. Feature Integration
All the ext racted features are integrated to get the final extracted image as result. Every b lock has a
similarity measure for each of the features. Hence after the feature extract ion process each instance (block) is a
sequence of 1s (YES) and 0s (NO) of length equal to the number of features extracted. Co mbin ing these extracted
features is synonymous to forming rules. One rule that co mbines the three feature s is color & edge | textures ,
which means color AND edge OR textu re. Depending on the features used and the domain, the rules vary. If a
particular feature is very accurate for a do main, then the rule will assign the class label as YES (1) (1 in the table
on the left). For those instances when I Class is not certain the class label is 2. Th is denotes uncertain regions that
may or may not be existed. The same rule used during the training phase is also used in the testing phase.
Table1
Rules for Feature Integration
Rule Name Col or Texture shape
Class-1 1 0 1
Class-2 0 0 0
Class-3 1 1 0
Class-4 1 0 0
If there are 3 features, for example, the above table shows a part of a set of rules that could be used. The first and
21
5. Feature Extraction For Image Retrieval Using Image Mining...
third rules say that color along with texture or edge conclusively determines that query image is present in that
block. The second rule says that when none of the features is 1 then query image is absent for sure. Fourth rule
states that color on its own is uncertain in determining the presence of query image.
V. Results and Discussions
The image database has used to retrieve the relevant images based on query image. The test image database
contains 200 images of 10 categories like structured and unstructured, sports images, missile images etc. Image
retrieval was performed on combinational feature set of primit ive features like colo r, texture and shape. Results
are obtained fro m class 1 feature set of primitive feature co lor, class 2 feature set of integrated feature color and
shape and class 3 feature set of integrated feature texture and shape. For single query mis sile image the results
shown in the following figures.
Figure 1.Results obtained from Primitive Feature Color
(a) (b)
Figure 2. Results obtained from Integrated Feature Extract ion (a)colo r and texture ,(b)color and shape
Based on commonly used performance measures in information retrieval, two statistical measures were computed
to assess system perfo rmance namely Recall and Precision. Fo r good retrieval system ideal values for recall is 1
and precision is of low value. The table 2 will give the performance evaluation of image retrieval based on
primitive feature color on 10 image databases .
22
6. Feature Extraction For Image Retrieval Using Image Mining...
Table 2
Performance Analysis based on Primit ive Feature (Co lor)
Query Retrieved Relevant Relevent Precision Recall
database Images Images Images retrieved
Img1 9 5 5 0.444 1
Img2 9 7 6 0.666 0.857
Img3 9 5 5 0.555 1
Img4 9 6 6 0.667 1
Img5 9 6 6 0.667 1
Img6 9 5 4 0.444 0.8
Img7 9 8 6 0.667 0.75
The table 2 will g ive the performance evaluation of integrated features of different classes like color and texture,
color and shape.
Table 3
Performance analysis based on integrated features of color and texture and color and shape
Query Integrated features of Integrated features of
database color and texture color and shape
Precision Recall Precision Recall
Img1 0.833 1 1 0.8
Img2 0.833 0.714 0.75 0.428
Img3 0.833 0.8 0.75 0.6
Img4 1 1 1 0.667
Img5 0.833 0.833 1 0.667
Img6 0.667 0.8 1 0.8
Img7 0.83 0.625 0.75 0.375
The performance analysis of image ret rieval system is based on primit ive feature color and integrated
features color and texture and color and shape. By taking 10 query images fro m different databases, the results are
analysed based on number of relevant images retrieved and total relevant images existed in the data base. The
performance of image retrieval system o f integrated feature is more when co mpared with primit ive feature
extraction.
VI. Conclusions and Future Enhancements
This paper elucidates the potentials of ext raction of features of the image using colo r, texture and shape
for ret riev ing the images fro m the specific image databases. The images are retrieved fro m the g iven database of
images by giving the query image. These results are based on various digital images of dataset. The performance
of the image retrieval was assessed using the parameters recall rate and precision. It was ascertained that the recall
rate and precision are high when the image retrieval was based on the feature integration on all the three features
the color, texture and shape than primitive features alone. Th e work can be extended fu rther on huge data bases for
retriev ing relevant images to obtain objects using different combination of weight for co lor and texture and shape
features.
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Biography
M.Madhu Bala.
She is doing her Ph.D in Co mputer Science and Engineering in the area of image mining at
Jawaharlal Nehru Technological Un iversity, Hyderabad. Her research interests are DataMining,
Image Analysis, Informat ion Retrieval Systems.. She holds the Life M embership of ISTE and CSI.
Dr. M.Seetha.
She had completed Ph.D in Computer Science and Engineering in the area of image processing
in December 2007 from Jawaharlal Nehru Technological University, Hyderabad and M . S. from B I T S,
Pilani in 1999. Her research interest includes image processing, neural networks, computer networks ,
artificial intelligence and data mining. She had about 10 papers published in refereed journals and more than
50 papers in the proceedings of National/International Conference and Symposiums. She was the recipient of
the AICTE Career Award for Young Teachers (CAYT) in FEB, 2009, and received the grant upto 10.5
lakhs over a period of three years by AICTE, INDIA. She was a reviewer for various International
Journals/Conferences. She holds the Life M embership of ISTE, IETE and CSI.
24