Thursday 28th of March 2024
 

Statistical Moments Extracted from Eight Bins Formed by CG Partitioning of Histogram Modified using Linear Equations


H. B. Kekre and Kavita Sonawane

This paper explores the novel idea for feature extraction based on bins approach for CBIR. This work has fulfilled all the criterion of efficient feature extraction method like dimensionality reduction, fast extraction and efficient retrieval. Main idea used in feature extraction is based on the histogram and the linear functions used to modify it. Each BMP image in database is separated in R, G and B planes. We have calculated the histogram for each of them which are modified using linear equations. These modified histograms are partitioned using CG so that mass of intensities of the image pixels will be distributed uniformly in two parts. This CG partitioning of three image planes leads to generate the eight bins. Information extracted from these bins is in the form of statistical first four absolute moments namely MEAN (MEAN), Standard deviation (STD), Skewness (SKEW), and Kurtosis (KURTO). Each of these moments are computed separately for R, G and B colors. This generates four types of feature vectors of dimension eight. Database of 2000 BMP images is used for the experimentation. Multiple feature vector databases are prepared as part of preprocessing work of this CBIR system. Comparison of query and database feature vector is carried out using three similarity measures namely Euclidean distance (ED), Absolute distance (AD) and Cosine correlation distance (CD). To evaluate the retrieval efficiency of this system we have used three parameters, Precision Recall Cross over Point (PRCP), Longest String (LS) and Length of String to Retrieve all Relevant (LSRR).

Keywords: Bins, CBIR, Histogram modification, Statistical moments, MEAN, STD, SKEW, KURTO, ED, AD, CD, PRCP, LS, LSRR

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ABOUT THE AUTHORS

H. B. Kekre
Dr. H. B. Kekre has received B.E. (Hons.) in Telecomm. Engg.from Jabalpur University in 1958,M.Tech (Industrial Electronics) from IIT Bombay in 1960, M.S. Engg. (Electrical Engg.)from University of Ottawa in 1965 and Ph.D. (System Identification) from IIT Bombay in 1970. He has worked Over 35 years as Faculty of Electrical Engineering and then HOD Computer Science and Engg. at IIT Bombay. For last 13 years worked as a Professor in Department of Computer Engg. atThadomalShahani Engineering College, Mumbai. He is currently Senior Professor working with Mukesh Patel School of Technology Management and Engineering, SVKM’s NMIMS University, Vile Parle(w), Mumbai, INDIA. He has guided 17 Ph.D.s, 150 M.E./M.Tech Projects and several B.E./B.Tech Projects. His areas of interest are Digital Signal processing, Image Processing and Computer Networks. He has more than 450 papers in National / International Conferences / Journals to his credit. Recently twelve students working under his guidance have received best paper awards. Five of his students have been awarded Ph. D. of NMIMS University. Currently he is guiding eight Ph.D. students. He is member of ISTE and IETE.

Kavita Sonawane
Ms. Kavita V. Sonawane has received M.E (Computer Engineering) degree from Mumbai University in 2008, currently Pursuing Ph.D. from Mukesh Patel School of Technology, Management and Engg, SVKM’s NMIMS University, Vile-Parle (w), Mumbai, INDIA. She has more than 8 years of experience in teaching. Currently working as a Assistant professor in Department of Computer Engineering at St. Francis Institute of Technology Mumbai. Her area of interest is Image Processing, Data structures and Computer Architecture. She has 16 papers in National/ International conferences / Journals to her credit. She is member of ISTE.


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