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Effect of Similarity Measures for CBIR using Bins Approach
H. B. Kekre, Kavita Sonawa
Pages - 182 - 197     |    Revised - 15-05-2012     |    Published - 20-06-2012
Volume - 6   Issue - 3    |    Publication Date - June 2012  Table of Contents
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KEYWORDS
Minkowski Distance, Correlation Distance, Moments, LSRR, PRCP, Longest String
ABSTRACT
This paper elaborates on the selection of suitable similarity measure for content based image retrieval. It contains the analysis done after the application of similarity measure named Minkowiski Distance from order first to fifth. It also explains the effective use of similarity measure named correlation distance in the form of angle ‘cosè’ between two vectors. Feature vector database prepared for this experimentation is based on extraction of first four moments into 27 bins formed by partitioning the equalized histogram of R, G and B planes of image into three parts. This generates the feature vector of dimension 27. Image database used in this work includes 2000 BMP images from 20 different classes. Three feature vector databases of four moments namely Mean, Standard deviation, Skewness and Kurtosis are prepared for three color intensities (R, G and B) separately. Then system enters in the second phase of comparing the query image and database images which makes of set of similarity measures mentioned above. Results obtained using all distance measures are then evaluated using three parameters PRCP, LSRR and Longest String. Results obtained are then refined and narrowed by combining the three different results of three different colors R, G and B using criterion 3. Analysis of these results with respect to similarity measures describes the effectiveness of lower orders of Minkowiski distance as compared to higher orders. Use of Correlation distance also proved its best for these CBIR results.
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4 Kekre, H. B., & Sonawane, K. Comparative Performance of Linear and CG Based Partitioning Of Histogram for Bins Formation in CBIR.
5 Sulayman, E. N., Ammar, M., & Hossein, J. Analysis study of Content Based Medical Image Retrieval Systems.
6 Kekre, H. B., & Sonawane, K. (2013, January). Use of equalized histogram CG on statistical parameters in bins approach for CBIR. In Advances in Technology and Engineering (ICATE), 2013 International Conference on (pp. 1-6). IEEE.
7 Kekre, H. B., & Sonawane, K. (2013). Performance evaluation of bins approach in YCbCr color space with and without scaling. International Journal of Soft Computing and Engineering, 3(3), 203-210.
8 Kekre, H. B., & Sonawane, M. K. Linear Equation in Parts as Histogram Specification for CBIR Using Bins Approach. International Journal of Engineering Research and Development e-ISSN, 73-85.
9 Kekre, H. B., & Sonawane, K. V. (2012). Histogram Specification with Higher Order Polynomial Functions over R, G and B Planes for CBIR Using Bins. International Journal of Advanced Research in Computer Science, 3(4).
10 Kekre, H. B., & Sonawane, K. (2012). Bins Approach for CBIR by Shifting the Histogram to Lower Intensities Using Proposed Polynomials. Signal & Image Processing, 3(4), 105.
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Dr. H. B. Kekre
NMIMS UNIVERSITY - India
hbkekre@yahoo.com
Dr. Kavita Sonawa
- India


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