Home   >   CSC-OpenAccess Library   >    Manuscript Information
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
Minkowski Distance, Correlation Distance, Moments, LSRR, PRCP, Longest String
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.
CITED BY (10)  
1 Vinay, S. K. (2016). Bins Approach To Content Based Image Retrieval.
2 Gupta, V., & Gupta, V. Nav view search.
3 Kekre, H. B., & Sonawane, K. (2013). Role of Histogram Modification Functions EQH, LOG, POLY, Linear Equations for CBIR Based on 64 Bins Approach. International Journal of Advanced Science and Technology, 59, 53-70.
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.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
Arnold W.M. Smeulders, Senior Member, IEEE, Marcel Worring, Simone Santini, Member, IEEE, Amarnath Gupta, Member, IEEE, and Ramesh Jain, Fellow, IEEE , “ Content-Based Image Retrieval at the End of the Early Years”.
C. W.Ngo, T. C. Pong, R.T. Chin. “Exploiting image indexing techniques in DCT domain” IAPR International Workshop on multimedia Media Information Analysis and Retrieval.
Dengsheng Zhang and Guojun Lu “Evaluation Of Similarity Measurement For Image Retrieval” www. Gscit.monash.edu.au/~dengs/resource/papers/icnnsp03.pdf.
Dr. H. B. Kekre, Dhirendra Mishra “Image Retrieval using DST and DST Wavelet Sectorization”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, 2011.
Dr. H. B. Kekre, Kavita Sonawane,“ Image Retrieval Using Histogram Based Bins of Pixel Counts and Average of Intensities”, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 10, No.1, 2012
Dr. H.B. Kekre, Mr. Dhirendra Mishra, Mr. Anirudh Kariwala , “Survey Of Cbir Techniques And Semantics”. International Journal of Engineering Science and Technology (IJEST), Vol. 3 No. 5 May 2011.
Dr. H.B.Kekre , Kavita Sonawane, Bins Approach To Image Retrieval Using Statistical Parameters Based On Histogram Partitioning Of R, G, B Planes, Jan 2012. ©IJAET ISSN: 2231-1963.
Dr. H.B.Kekre, Dr. Sudeep D. Thepade, Shrikant P. Sanas, Sowmya Iyer , “Shape Content Based Image Retrieval using LBG VectorQuantization.” (IJCSIS) International Journal of Computer Science and Information Security,Vol. 9, No. 12, December 2011.
Elif Albuz, Erturk Kocalar, and Ashfaq A. Khokhar, “ Scalable Color Image Indexing And Retrieval Using Vector Wavelets” IEEE Transactions on Knowledge and Data Engineering, Volume 13 Issue 5, September 200.
Ellen Spertus, Mehran Sahami, Orkut Buyukkokten, “Evaluating Similarity Measures:A LargeScale Study in the Orkut Social network“ Copyright 2005ACM.The definitive version was published in KDD ’05, August 2124, 2005http://doi.acm.org/10.1145/1081870.1081956.
Gang Qian, Shamik Sural, Yuelong Gu† Sakti Pramanik, “Similarity between Euclidean and cosine angle distance fornearest neighbor queries“, SAC’04, March 14-17, 2004, Nicosia, Cyprus Copyright 2004 ACM 1-58113-812-1/03/04.
Guang Yang, Yingyuan Xiao, “A Robust Similarity Measure Method in CBIR System“ 978- 0-7695-3119-9/08 $25.00 © 2008 IEEE, 2008 Congress on Image and Signal Processing.
H. B. Kekre , Kavita Sonawane, Query Based Image Retrieval Using kekre’s, DCT and Hybrid wavelet Transform Over 1st and 2nd Moment, International Journal of Computer Applications (0975 – 8887), Volume 32– No.4, October 2011.
H. B. Kekre , Kavita Sonawane, “Feature Extraction in Bins Using Global and Local thresholding of Images for CBIR” International Journal Of Computer Applications In Applications In Engineering, Technology And Sciences, ISSN: 0974-3596, October ’09 – March ’10, Volume 2 : Issue 2
H. B. Kekre, Kavita Sonawane, Retrieval of Images Using DCT and DCT Wavelet Over Image Blocks. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 10, 2011.
H.B.Kekre, Sudeep D. Thepade, Tanuja K. Sarode, Shrikant P. Sanas “Image Retrieval Using Texture Features Extracted Using Lbg, Kpe, Kfcg, Kmcg, Kevr With Assorted Color Spaces”, International Journal of Advances in Engineering & Technology, Jan 2012.©IJAET ISSN: 2231-1963 520 Vol. 2, Issue 1, pp. 520-531.
Hualu Wang, Ajay Divakaran, Anthony Vetro, Shih-Fu Chang, and Huifang Sun “Survey of compressed-domain features used in audio-visual indexing and analysis” 2003 Elsevier Science (USA). All rights reserved.doi:10.1016/S1047-3203(03)00019-1.
Image Retrieval Using BDIP and BVLC Moments, Young Deok Chun, Sang Yong Seo, and Nam Chul Kim, IEEE Transactions On Circuits And Systems For Video Technology, Vol. 13, No. 9, September 2003.
John P., Van De Geer, “Some Aspects of Minkowski distance”, Department of data theory, Leiden University. RR-95-03.
Julia Vogela, Bernt Schiele, “Performance evaluation and optimization for content-based image retrieval“, 0031-3203/$30.00 _ 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.patcog.2005.10.024
Mann-Jung HsiaoYo-Ping HuangTe-Wei Chiang, “A Region-Based Image Retrieval Approach Using Block DCT” 0-7695-2882-1/07 $25.00 ©2007 IEEE.
Raimondo Schettini, G. Ciocca, S. Zuffi, “A Survey Of Methods For Color Image Indexing And Retreival In Image Databases”. www.intelligence.tuc.gr/~petrakis/courses/.../papers/color-survey.pdf
S. Nandagopalan, Dr. B. S. Adiga, and N. Deepak, “ A Universal Model for Content-Based Image Retrieval” World Academy of Science, Engineering and Technology 46 2008.
Sameer Antania, Rangachar Kasturia; , Ramesh Jainb “A surveyon the use of pattern recognition methods for abstraction, indexing and retrieval of images and video” Pattern Recognition 35 (2002) 945–965.
Sang-Hyun Park, Hyung Jin Sung, “Correlation Based Image Registration for Pressure Sensitive Paint“, flow.kaist.ac.kr/upload/paper/2004/SY2004.pdf .
Simone Santini, Member, IEEE, and Ramesh Jain, Fellow, IEEE, “Similarity Measures” IEEETransactions On Pattern Analysis And Machine Intelligence, Vol. 21, No. 9, September 1999.
Stéphane Marchand-Maillet, “Performance Evaluation in Content-based Image Retrieval:The Benchathlon Network”,
Thomas Deselaers, Daniel Keysers, and Hermann Ney , “Classification Error Rate for Quantitative Evaluation of Content-based Image Retrieval Systems” http://www.cs.washington.edu/research/imagedatabase/groundtruth/, and http://wwwi6. informatik.rwthaachen.de/˜deselaers/uwdb
Wu Xi, Zhu Tong, “Image Retrieval based on Multi-wavelet Transform” 978-0-7695-3119- 9/08 $25.00 © 2008 IEEE, 2008 Congress on Image and Signal Processing.
Yong Rui and Thomas S. Huang , “Image Retrieval: Current Techniques, Promising Directions, and Open Issues” Journal of Visual Communication and Image Representation 10, 39–62 (1999).
Young Deok Chun, Sang Yong Seo, and Nam Chul Kim ,“Image Retrieval Using BDIP and BVLC Moments”, IEEE Transactions On Circuits And Systems For Video Technology, Vol. 13, No. 9, September 2003.
Zhi-Hua Zhou Hong-Bin Dai, “Query-Sensitive Similarity Measure for Content-Based Image Retrieval”ICDM, 06, Proceeding’s of sixth International Conference on data Mining. IEEE Comp. Society, Washington, DC, USA 2006.
Zur Erlangung des Doktorgradesder Fakult, Angewandte Wissenschaften “Feature Histograms for Content-Based Image Retrieval”2002
Dr. H. B. Kekre
Dr. Kavita Sonawa
- India