This document describes a content-based image retrieval system that extracts shape and texture features from images. It uses the HSV color space and wavelet transform for feature extraction. Color features are extracted by quantizing the H, S, and V components of HSV into unequal intervals based on human color perception. Texture features are extracted using wavelet transforms. The color and texture features are then combined to form a feature vector for each image. During retrieval, the similarity between a query image and images in the database is measured using the Euclidean distance between their feature vectors. The results show that retrieving images using HSV color features provides more accurate results and faster retrieval times compared to using RGB color features.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
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.
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.
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.
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
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
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.
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.
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.
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
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.
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.
C OMPARATIVE S TUDY OF D IMENSIONALITY R EDUCTION T ECHNIQUES U SING PCA AND ...csandit
The aim of this paper is to present a comparative s
tudy of two linear dimension reduction
methods namely PCA (Principal Component Analysis) a
nd LDA (Linear Discriminant Analysis).
The main idea of PCA is to transform the high dimen
sional input space onto the feature space
where the maximal variance is displayed. The featur
e selection in traditional LDA is obtained
by maximizing the difference between classes and mi
nimizing 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 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 based
dimension reduction method gives the
better performance in terms of higher precision and
recall values with lesser computational
complexity than the LDA based method.
An implementation of novel genetic based clustering algorithm for color image...TELKOMNIKA JOURNAL
The color image segmentation is one of most crucial application in image processing. It can apply to medical image segmentation for a brain tumor and skin cancer detection or color object detection on CCTV traffic video image segmentation and also for face recognition, fingerprint recognition etc. The color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper the, L*a*b color space conversion has been used to reduce the one dimensional and geometrically it converts in the array hence the further one dimension has been reduced. The a*b space is clustered using genetic algorithm process, which minimizes the overall distance of the cluster, which is randomly placed at the start of the segmentation process. The segmentation results of this method give clear segments based on the different color and it can be applied to any application.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
In this project, we proposed a Content Based Image Retrieval (CBIR) system which is used to retrieve a
relevant image from an outsized database. Textile images showed the way for the development of CBIR. It
establishes the efficient combination of color, shape and texture features. Here the textile image is given as
dataset. The images in database are loaded. The resultant image is given as input to feature extraction
technique which is transformation of input image into a set of features such as color, texture and shape.
The texture feature of an image is taken out by using Gray level co-occurrence matrix (GLCM). The color
feature of an image is obtained by HSI color space. The shape feature of an image is extorted by sobel
technique. These algorithms are used to calculate the similarity between extracted features. These features
are combined effectively so that the retrieval accuracy and recall rate is enhanced. The classification
techniques such as Support Vector Machine (SVM) are used to classify the features of a query image by
splitting the group such as color, shape and texture. Finally, the relevant images are retrieved from a large
database and hence the efficiency of an image is plotted.The software used is MATLAB 7.10 (matrix
laboratory) which is built software applications
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
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
Evaluation of Euclidean and Manhanttan Metrics In Content Based Image Retriev...IJERA Editor
Content-based Image Retrieval is all about generating signatures of images in database and comparing the signature of the query image with these stored signatures. Color histogram can be used as signature of an image and used to compare two images based on certain distance metric. Distance metrics Manhattan distance (L1 norm) and Euclidean distance (L2 norm) are used to determine similarities between a pair of images. In this paper, Corel database is used to evaluate the performance of Manhattan and Euclidean distance metrics. The experimental results showed that Manhattan showed better precision rate than Euclidean distance metric. The evaluation is made using Content based image retrieval application developed using color moments of the Hue, Saturation and Value(HSV) of the image and Gabor descriptors are adopted as texture features.
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.
Automatic dominant region segmentation for natural imagescsandit
Image Segmentation segments an image into different homogenous regions. An efficient
semantic based image retrieval system divides the image into different regions separated by
color or texture sometimes even both. Features are extracted from the segmented regions and
are annotated automatically. Relevant images are retrieved from the database based on the
keywords of the segmented region In this paper, automatic image segmentation is proposed to
obtained dominant region of the input natural images. Dominant region are segmented and
results are obtained . Results are also recorded in comparison to JSEG algorithm
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.
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.
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.
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.
C OMPARATIVE S TUDY OF D IMENSIONALITY R EDUCTION T ECHNIQUES U SING PCA AND ...csandit
The aim of this paper is to present a comparative s
tudy of two linear dimension reduction
methods namely PCA (Principal Component Analysis) a
nd LDA (Linear Discriminant Analysis).
The main idea of PCA is to transform the high dimen
sional input space onto the feature space
where the maximal variance is displayed. The featur
e selection in traditional LDA is obtained
by maximizing the difference between classes and mi
nimizing 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 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 based
dimension reduction method gives the
better performance in terms of higher precision and
recall values with lesser computational
complexity than the LDA based method.
An implementation of novel genetic based clustering algorithm for color image...TELKOMNIKA JOURNAL
The color image segmentation is one of most crucial application in image processing. It can apply to medical image segmentation for a brain tumor and skin cancer detection or color object detection on CCTV traffic video image segmentation and also for face recognition, fingerprint recognition etc. The color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper the, L*a*b color space conversion has been used to reduce the one dimensional and geometrically it converts in the array hence the further one dimension has been reduced. The a*b space is clustered using genetic algorithm process, which minimizes the overall distance of the cluster, which is randomly placed at the start of the segmentation process. The segmentation results of this method give clear segments based on the different color and it can be applied to any application.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
In this project, we proposed a Content Based Image Retrieval (CBIR) system which is used to retrieve a
relevant image from an outsized database. Textile images showed the way for the development of CBIR. It
establishes the efficient combination of color, shape and texture features. Here the textile image is given as
dataset. The images in database are loaded. The resultant image is given as input to feature extraction
technique which is transformation of input image into a set of features such as color, texture and shape.
The texture feature of an image is taken out by using Gray level co-occurrence matrix (GLCM). The color
feature of an image is obtained by HSI color space. The shape feature of an image is extorted by sobel
technique. These algorithms are used to calculate the similarity between extracted features. These features
are combined effectively so that the retrieval accuracy and recall rate is enhanced. The classification
techniques such as Support Vector Machine (SVM) are used to classify the features of a query image by
splitting the group such as color, shape and texture. Finally, the relevant images are retrieved from a large
database and hence the efficiency of an image is plotted.The software used is MATLAB 7.10 (matrix
laboratory) which is built software applications
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
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
Evaluation of Euclidean and Manhanttan Metrics In Content Based Image Retriev...IJERA Editor
Content-based Image Retrieval is all about generating signatures of images in database and comparing the signature of the query image with these stored signatures. Color histogram can be used as signature of an image and used to compare two images based on certain distance metric. Distance metrics Manhattan distance (L1 norm) and Euclidean distance (L2 norm) are used to determine similarities between a pair of images. In this paper, Corel database is used to evaluate the performance of Manhattan and Euclidean distance metrics. The experimental results showed that Manhattan showed better precision rate than Euclidean distance metric. The evaluation is made using Content based image retrieval application developed using color moments of the Hue, Saturation and Value(HSV) of the image and Gabor descriptors are adopted as texture features.
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.
Automatic dominant region segmentation for natural imagescsandit
Image Segmentation segments an image into different homogenous regions. An efficient
semantic based image retrieval system divides the image into different regions separated by
color or texture sometimes even both. Features are extracted from the segmented regions and
are annotated automatically. Relevant images are retrieved from the database based on the
keywords of the segmented region In this paper, automatic image segmentation is proposed to
obtained dominant region of the input natural images. Dominant region are segmented and
results are obtained . Results are also recorded in comparison to JSEG algorithm
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.
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.
The project aims at development of efficient segmentation method for the CBIR system. Mean-shift segmentation generates a list of potential objects which are meaningful and then these objects are clustered according to a predefined similarity measure. The method was tested on benchmark data and F-Score of .30 was achieved.
Color and texture based image retrievaleSAT Journals
Abstract Content-based image retrieval (CBIR) is an vital research area for manipulating bulky image databases and records. Alongside the conventional method where the images are searched on the basis of words, CBIR system uses visual contents to retrieve the images. In content based image retrieval systems texture and color features have been the primal descriptors. We use HSV color information and mean of the image as texture information. The performance of proposed scheme is calculated on the basis of precision, recall and accuracy. As an effect, the blend of color and texture features of the image provides strong feature set for image retrieval. Keywords: image retrieval, HSV color space, color histogram, image texture.
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.
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.
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.
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
Research Inventy : International Journal of Engineering and Scienceinventy
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.
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.
The content based Image Retrieval is the restoration of images with respect to the visual appearances
like texture, shape and color.The methods, components and the algorithms adopted in this content based
retrieval of images were commonly derived from the areas like pattern identification, signal progressing
and the computer vision. Moreover the shape and the color features were abstracted in the course of
wavelet transformation and color histogram. Thus the new content based retrieval is proposed in this
research paper.In this paper the algorithms were required to propose with regards to the shape, shade and
texture feature abstraction .The concept of discrete wavelet transform to be implemented in order to
compute the Euclidian distance.The calculation of clusters was made with the help of the modified KMeans
clustering technique. Thus the analysis is made in among the query image and the database
image.The MATLAB software is implemented to execute the queries. The K-Means of abstraction is
proposed by performing fragmentation and grid-means module, feature extraction and K- nearest neighbor
clustering algorithms to construct the content based image retrieval system.Thus the obtained result are
made to compute and compared to all other algorithm for the retrieval of quality image features
Content-Based Image Retrieval Using Modified Human Colour Perception Histogram cscpconf
This paper proposes Modified Human Colour Perception (MHCPH) based on human visual
perception. The colour and gray weights are distributed to neighbouring bins smoothly with
respect to pixel information. The amount of weight distributed to the neighbouring bins is
estimated using NBS distance, which is for human visual perception of colour. This distribution
makes it possible to extract the background colour information effectively along with the
foreground information. The low-level feature of all the database images are extracted and
stored in feature database. The relevant images are retrieved for a query based on the similarity
ranking between the query and database images. In this work, Manhattan distance is used as
distance metric. The experimental results are promising and show that the proposed approach identifies relevant images based on the level of smooth distribution even for an image with complex background colour.
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.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
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Content based image retrieval based on shape with texture features
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Content Based Image Retrieval based on Shape with Texture
Features
Capt. Dr. S. Santhosh Baboo
Reader, Postgraduate & Research, Department of Computer Applications
DG Vaishnava College, Chennai - 600 106, Tamil Nadu, India.
E-mail: santhos2001@sify.com
Sudhir Ramadass
Research Scholar of Manonmaniam Sundaranar University
E-mail: sudhir.ramadas@gmail.com
Abstract
In areas of state, domain and hospitals, massive collections of digital pictures are being created. These image
collections are the merchandise of digitizing existing collections of analogue images, diagrams, drawings,
paintings, and prints. Retrieving the specified similar image from a large dataset is very difficult. A new image
retrieval system is obtainable in this paper, for feature extraction HSV color space and wavelet transform
approach are used. Initially constructed one dimension feature vector and represented the color feature it is made
by that the color space is quantified in non-equal intervals. Then with the help of wavelet texture feature
extraction is obtained. At last by using of wavelet transform combined the color feature and texture feature
method. The illustration features are susceptible for different type images in image retrieval experiments. The
color features opted to the rich color image with simple variety. Texture feature opted to the complex images. At
the same time, experiments reveal that HSV texture feature based on wavelet transform has better effective
performance and stability than the RGB. The same work is performed for the RGB color space and their results
are compared with the proposed system. The result shows that CBIR with the HSV color space is retrieves image
with more accuracy and reduced retrieval time.
Keywords--Content Based Image Retrieval, HSV, RGB
Introduction
CONTENT-based image retrieval is used to investigate and examine the actual contents of the image. In this
perspective expression ‘content’ related to colors, shapes, textures, or any other information that can be derived
from the image itself. Without the skill to examine image content, searches must rely on metadata such as
captions or keywords, which may be laborious or expensive to produce. Different methods of content – based
retrieval methods are available and it is color, texture and shape. In this paper, HSV Color Space and Texture
Features based Image retrieval is proposed. Selection for this method is based on two reasons. In first stage to
make very good retrieval presentation by using of color based system. The reason is to select this is because of
very simple implementation. Unlike the shape based methods and texture based, it doesn’t require image
segmentation which itself is a hard image processing problem.
Comparison of CBIR with already established method using text index, this method find out and get the image
visual characteristics it directly establishes index in accordance with the characteristics of image information.
Method of image retrieval is obtained through their similarity of image features. CBIR is a highly challenging
problem for general-purpose image databases for the reason of large size database [1, 2, 12]. The most
significant visual features are Color and texture. In the field of computer graphics, multimedia, computational
science HSV color space is used. [3]. Discriminate colors in this space are used by Hue, perceived light intensity
are called as value then by adding percentage of white light to a pure color is called saturation. The capability to
separate chromatic and achromatic components by using of HSV color space it is one of the advantages for this.
Therefore, this paper prefers the HSV color space to extract the color features based on hue, saturation and value.
Texture feature does not depend on color or intensity and reflects the essential occurrence of images it is one of
the class of visual characteristics.
To develop the accuracy of HSV depends on the combination of both color and texture features. The rest of
the paper is organized as follows. Section II gives the literature survey. Section III discusses the RGB and HSV
feature extraction techniques. Section IV displays the experimental results and the paper is concluded in section
V.
Literature Survey
In the literature survey, several methods have been proposed for the content based image retrieval with the
HSV color space and texture features. Among the most recently published works are those presented as follows
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S. Sclaroff et.al, [4] proposed a CBIR technique. Image Rover may be a search by image content navigation
tool for the globe wide internet that is WWW. To assemble images inadvisably, the image assortment scheme
utilizes a distributed fleet of World Wide Web robots running on completely different computers. The image
robots gather data concerning the images they notice, computing the acceptable image decompositions and
indices, and store this extracted data in vector type for searches supported image content. At search time, users
will iteratively guide the search through the choice of relevant examples. Search performance is created
economical through the utilization of approximate, optimized k-d tree algorithmic rule. The system employs a
completely unique relevancy feedback algorithmic rule that selects the distance metrics acceptable for a selected
query
J. Vogel et.al, [5] investigated the face recognition problem through energy histogram of the DCT
coefficients. There are various recognition performances are discussed, distinctly the histogram bin sizes and
feature sets are consider. In addition to this the author proposed a method of choosing a classification threshold
incrementally. Yale face database are taken for experimental and results indicated that the threshold obtained via
the proposed technique provides a balanced recognition in term of precision and recall. In addition to this it
demonstrated that the energy histogram algorithm outperformed the well-known Eigen face algorithm.
A. Pentland et.al, [6] described an idea to create features from an image database for use in indexing and
retrieving. Most important regions that suddenly attract the eye are large color regions that usually control an
image. Allowing searching the image by using features obtained from here that are similar perceptually. By
human psychophysical measurements of color appearance, algorithm of multiband smoothing are generated.
From this multiscale representation of the image are considering using this author calculate the color features
and Gabor color texture features on regions. The combined feature vector is then worn for indexing all salient
regions of an image. Using a multipass retrieval and ranking mechanism retrieval images are selected that had
more similar regions to the query image. Matches are found using the L2 metric. The results displays that the
proposed method performs very well.
J. Wang et.al, [7] projected a fuzzy logic approach, UFM (unified feature matching), for region-based image
retrieval. For this recovery system, an image are represented as a set of segmented regions, in which color,
texture, and shape properties of each image are reflected by a fuzzy feature. Finally an image is related with a
family of fuzzy features corresponding to regions. Transition between regions (blurry boundaries) within an
image by using of fuzzy features and incorporate the segmentation-related uncertainties into the retrieval
algorithm. The overall similarity between two families of fuzzy features are known as similarities of two images
and then it is measured by a similarity measure, UFM measure, which integrates properties of all the regions in
the images. Compared with similarity measures which are based on individual regions and on all regions with
crisp-valued feature representations and provides a very intuitive quantification and greatly reduces the influence
of inaccurate segmentation by using of UFM measure. The UFM has been implemented as a element of the
experimental simplicity image retrieval system. The presentation of the system is pictorial by means of examples
from an image database of about 60,000 general-purpose images
Methodology
First the feature extraction in RGB image is explained. An image IWxH of a 2-dimensional array of pixels is
considered. There are W columns and H rows in each image. Each pixel is a triple comprising the RGB values of
the color that it represents. Hence, the image has three color components:
Feature extraction in RGB
Selecting gradient-based features makes the scheme robust to illumination variations whereas use of
orientation information to define features provides robustness against contrast variations. Basic idea behind these
features is to split an image into tiles called cells and then extract a weighted histogram of gradient orientations
for each cell. Following subsections provide details of each step.
Defining multiple resolutions
Since there may be difference in resolution between images used for training the classifier, and those of new
target images, features should be extracted from an image at multiple levels of resolution. Resolution level is
found by a shrinkage factor
that describes the amount by which the image size is reduced in each dimension, as compared to
the size in previous level. Consequently, the size of an image in level l is such that,
Features are extracted from cells at each resolution level from 0 to an upper limit
L. An image is represented at level l as which comprises three color channels
Gradient computation
For image at each level l, a gradient image is determined as follows:
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such that
And
Here, (x, y) represents the location of a pixel such that 1 < x < w(l) and 1 < y < h(l). Gradient values for all
pixels available at the boundary of the image are defined to be zero (both magnitude and orientation).
It should be noted that mag(x, y) has only one component as it retains the maximum gradient magnitude
value amongst all color components at pixel (x,y). Similarly, (x, y) retains the orientation value for that color
component for pixel (x, y).
Computing Histogram of Gradient Orientations
Gradient orientation values lie between [0 Π]. This range can be discretized into 9 bins of size each. Now,
the image at each level is split into non-overlapping cells of size pxp. For each cell (cx; cy), a 9 element array is
computed f(cx; cy). Each of the elements belongs to one of the bins in which the orientation of a pixel in a cell
falls. Thus, each pixel is supposed to vote for one of the bins in the histogram. At that pixel, this vote is weighted
by the magnitude of the gradient. The following equation shows this:
Here 1 ≤ b ≤ 9 and I{.} is the identity function. Energy of a cell (cx; cy) as defined
Now, in order to retain spatial information between neighboring cells, features of neighboring cells are
appended to each cell features and also normalize all these features with the sum of energies of all neighboring
cells. This neighborhood of cells is called a block. 2x2 block are considered such that the top, left and top-left
cells are included in the neighborhood of a cell. Hence, an overall HOG feature vector is defined (at level l) for a
cell (cx; cy) as follows:
Here, j denotes concatenation of features. It should be noted here that the final HOG feature vector has a
dimension of 4x 9 = 36. It should also be noted that image at each level generates
number of cell features, as features from cells in the topmost row and in the leftmost column in image cannot be
generated because their left and top neighbors are not defined.
Feature extraction of HSV
Color Features of HSV
Because of a broad range of HSV each component that the computation is very difficult to ensure rapid
retrieval in the time of directly calculates the characteristics for retrieval image [8, 9]. So to reduce computation
and improve efficiency by quantify HSV space component to. With this to, doesn’t need to calculate all
segments by the limitation of the distinguish color of the human eye. Unequal interval quantization applied on H,
S, and V components based on the human color perception. Color is divided into eight parts based on the color
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model of substantial analysis. Based on the distinguish of the human eye saturation and intensity is divided into
three parts separately [10]. In accordance with the different colors and subjective color perception quantification,
quantified hue (H), saturation (S) and intensity (V) are showed in equation below.
That the quantified level for each H, S, V are discussed previously based on this one-dimensional feature
vector named G are formed by using of three-dimensional feature vector of H, S, V for different values of with
different weight to form:
Where QS is quantified series of S, QV is quantified series of V.
Here QS = QV = 3 is set, then
One-dimensional vector are formed in this way by using of three-component vector of HSV form, which
quantize the whole color space for the 72 kinds of main colors. So it can grip 72 bins of one-dimensional
histogram. By this type of quantification effectively reducing the images by the effects of light intensity and also
reducing the computational time and complexity.
As the components of feature vector may have different physical meaning entirely, their rate of change may be
very different. It will be much of the deviation of the calculation of the similarity if it is not normalized, so the
components are normalized to the same range. The process of normalization is to make the components of
feature vector equal importance.
A. Texture Primitive
Supposed I is a M × N image. The image is divided into m×m pixel non-overlap blocks. For each block,
the mean value µ and the standard deviation σ of gray in an image block is calculated according to
where p (x, y) is the gray value of the pixel located in (x, y) for image I . By the principle of BTC, for
those pixels in each block whose gray value is bigger than µ, we make them equal to “1”, otherwise, “0”. In this
way, a series of binary blocks are gained and the shape distribution is also expressed by these binary blocks to
some extent. In the experiment, the similar texture structure leads to the similar binary blocks.
In the extraction, we found that the different blocks may be lead to same texture value. As shown in
figure 3.1 (d) and figure 3.1 (e). Therefore, in this paper, a threshold β is adopted to avoid the question. Those
image blocks whose standard deviation is smaller than β are regard as even blocks and make the primitive value
as “0”. Otherwise, the primitive value is calculated according to above method. In the experiment, we adopted
the statistical methods. We found that it did not affect the performance of method when β = 0.0025σ. where, σ is
the mean value of gray for image block.
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Image
block
20 22
8 7
9 19
11 20
17 11
18 9
8 7
6 8
20 7
6 23
Binary
block
1 1
0 0
0 1
0 1
1 0
1 0
1 0
0 1
1 0
0 1
Binary
codes 1100 0101 1001 1001 1001
Texture
primitive
codes
12 5 10 9 9
(a) (b) (c) (d) (e)
Figure 3.1: Image Blocks and corresponding Texture Primitive
B. The Spatial Feature of the Texture Primitive
After defining the type of texture primitive, an image of M × N is corresponding to a matrix of [M
/m]×[N / m] expressed by P. P(x, y) is the index of the texture primitive which is located in (x, y) in P. To extract
the spatial information of the texture primitive, for certain kind of texture primitive in P(x, y), we kept its value
and make others equal to zero. The spatial distribution map of texture primitive is constructed. Based on the map,
the spatial feature is proposed
Suppose
be the set of points with index i in P and |Ai| be the number of elements in i A . Let Ci = (xi, yi) be the
centroid . Moreover, xi and yi are defined as follows
(3)
Let ri be the radius of the point whose index is i. The definition is given by
(4)
Therefore, the sum of the distance between all points whose index is i and centroid is defined as follow
Integration of the color and texture feature
Assuming that images A, B, extracted the normalized feature as:
Here, N is the scale of the feature. Through the similarity computation by Euclidean distance, design this
model:
Here 1 ω is the weight of color features, 2 ω is the weight of texture features, FC* represents the 72-
dimensional color features, FT* on behalf of 8-dimensional texture features. For a more precise measure of the
similarity between images, usually the calculated Euclidean distance has to be normalized, because Euclidean
distance range is [0, 2], normalized Euclidean distance is as follows:
The feature extraction methods for the RGB and HSV based images are explained. These features are used for
the further processing in the content based image retrieval techniques.
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Experimental Results
The dataset provided at webdocs.cs.ualberta.ca [11] is used to test the proposed method. Experimental images
cover a rich of content, including landscapes, animals, plants, monuments, transport (cars, planes) and so on.
Experiments show that ω1 =ω2 = 0.5, with better retrieval performance. Image retrieval based on texture feature,
the use of the above-mentioned method of similarity measure to calculate the texture feature distance between
the sample image and the library image. According to a similar distance from small to large with the image, the
smaller the distance, that is, the more similar. Two typical image retrieval examples are done by means of the
RGB and HSV. The result is displayed as follows.
Figure 2. Sample image given to test both RGB and HSV
Figure 3 gives the retrieved images when the RGB color space is used. The input image given is shown in
figure 2. Figure 4 shows the retrieved images when HSV color space is used. When comparing the HSV color
space used CBIR with RGB method, HSV have higher performance. The time consumed by RGB to retrieve
similar images is around 0.95 seconds. When using the HSV the same images are retrieved within 0.8 seconds.
Figure 3. Retrieved images when RGB Color space is used
Figure 4. Retrieved images when HSV Color space is used
Conclusion
This paper presents an approach supported HSV color space and texture characteristics of the image retrieval
and comparison with RGB color house. Through a range of statistics the experimental results will be finished
that a feature extraction methodology cannot adapt to any or all the photographs. Supported the color feature, the
made color images, like the sort of landscape pictures or planes, the color characteristics of the utilization of
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standard search colors will be the same as the image. The utilization of color and texture options of rippling
rework is additional appropriate for segmentation of objects and classification of connected image from
thousands of pictures. During which the retrieved pictures square measure a lot of similar once the HSV is
employed and therefore the retrieval time is additionally less once scrutiny with the RGB methodology.
REFERENCES
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[3] Y. Rui, T. Huang, S. Mehrotra, “Content-Based image retrieval with relevance feedback in MARS, ” In
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[4] S. Sclaroff, L. Taycher, and M. La Cascia, ImageRover: A content-based image browser for the world wide
web, IEEE Workshop on content-based access of image and video libraries (1997).
[5] J. Vogel and B. Schiele, On Performance Characterization and Optimization for Image Retrieval, 7th
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Lt. Dr. S. Santhosh Baboo, has around Seventeen years of postgraduate teaching experience in
Computer Science, which includes Six years of administrative experience. He is a member,
board of studies, in several autonomous colleges, and designs the curriculum of undergraduate
and postgraduate programmes. He is a consultant for starting new courses, setting up computer
labs, and recruiting lecturers for many colleges. Equipped with a Master degree in Computer
Science and a Doctorate in Computer Science, he is a visiting faculty to IT companies. It is
customary to see him at several national/international conferences and training programmes, both as a participant
and as a resource person. He has been keenly involved in organizing training programmes for students and
faculty members. His good rapport with the IT companies has been instrumental in on/off campus interviews,
and has helped the post graduate students to get real time projects. He has also guided many such live projects.
Lt. Dr. Santhosh Baboo has authored a commendable number of research papers in international/national
Conference/journals and also guides research scholars in Computer Science. Currently he is Reader in the
Postgraduate and Research department of Computer Science at Dwaraka Doss Goverdhan Doss Vaishnav
College (accredited at ‘A’ grade by NAAC), one of the premier institutions in Chennai.
Sudhir Ramadass received his Bachelor Degree in Computer Science, Master Degree in
Computer Applications from Bharathiar University, Coimbatore and Master of Philosophy in
Computer Science from Manonmaniam Sundaranar University, Thirunelveli. He has around
Eleven years of teaching experience in Computer Science & Applications. His research interest
is in the field of image mining, digital image retrieval. He is currently working as Assistant
Professor, Department of Computer Science, CMS College of Science & Commerce,
Coimbatore, Tamilnadu, India.
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