H. B. Kekre; Kavita Sonawane

Histogram Bins Matching Approach for CBIR Based on Linear grouping for Dimensionality Reduction

Full Text (PDF, 1196KB), PP.68-82

Views: 0 Downloads: 0

Author(s)

H. B. Kekre ^1,* Kavita Sonawane ¹

1. MSTME, NMIMS University, Mumbai, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2014.01.10

Received: 3 Jun. 2013 / Revised: 9 Aug. 2013 / Accepted: 25 Sep. 2013 / Published: 8 Nov. 2013

Index Terms

Histogram bins, linear grouping, count of pixels, total intensities Mean, PRCP, LS, LSRR

Abstract

This paper describes the histogram bins matching approach for CBIR. Histogram bins are reduced from 256 to 32 and 16 by linear grouping and effect of this dimensionality reduction is analyzed, compared, and evaluated. Work presented in this paper contributes in all three main phases of CBIR that are feature extraction, similarity matching and performance evaluation. Feature extraction explores the idea of histogram bins matching for three colors R, G and B. Histogram bin contents are used to represent the feature vector in three forms. First form of feature is count of pixels, and then other forms are obtained by computing the total and mean of intensities for the pixels falling in each of the histogram bins. Initially the size of the feature vector is 256 components as histogram with the all 256 bins. Further the size of the feature vector is reduced to 32 bins and then 16 bins by simple linear grouping of the bins. Feature extraction processes for each size and type of the feature vector is executed over the database of 2000 BMP images having 20 different classes. It prepares the feature vector databases as preprocessing part of this work. Similarity matching between query and database image feature vectors is carried out by means of first five orders of Minkowski distance and also with the cosine correlation distance. Same set of 200 query images are executed for all types of feature vector and for all similarity measures. Performance of all aspects addressed in this paper are evaluated using three parameters PRCP (Precision Recall Cross over Point), LS (longest string), LSRR (Length of String to Retrieve all Relevant images).

Cite This Paper

H. B. Kekre, Kavita Sonawane,"Histogram Bins Matching Approach for CBIR Based on Linear grouping for Dimensionality Reduction", IJIGSP, vol.6, no.1, pp. 68-82, 2014. DOI: 10.5815/ijigsp.2014.01.10

Reference

[1]Raimonodo Schettini, G. Ciocca, Silvia Zuffi, “Content-Based Image Retrieval at the End of the Early Years” Institute of Tecnology, Infomatiche Multimediali-In Color Imaging Science: Exploiting Digital , 2001.

[2]Arnold W.M, Marcel Worring, Simone Santini, “Content-Based Image Retrieval at the End of the Early Years”, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 22, No. 12, December 2000.

[3]V. N. REDDY, K. SATYA PRASAD, “Content Based Image Retrieval Using Local Derivative Patterns”, Journal of Theoretical and Applied Information Technology, 30th June 2011. Vol. 28 No.2, Publication of Little Lion Scientific R&D, Islamabad PAKISTA.

[4]SHI Dongcheng, XU Lan, HAN Ungyan, “Image retrieval using both color and texture features”, The Journal of China Universities Of Posts And Telecommunications Volume 14, Supplement, October 2007.

[5]Hui Yu, Mingjing Li, Hong-Jiang Zhang, “Color Texture Moments For Content-Based Image Retrieval”, Image Processing. 2002. Proceedings. 2002 International Conference on (Volume:3 ) 24-28 June 2002, ISSN :1522-4880, DOI: 10.1109/ICIP.2002.1039125.

[6]Bikesh Kr. Singh1, G. R Sinha, Bidyut Mazumdar “Content Based Retrieval of X- ray Images Using Fusion of Spectral Texture and Shape Descriptors”, 2010 International Conference on Advances in Recent Technologies in Communication and Computing, 978-0-7695-4201-0/10 $26.00 © 2010 IEEE.

[7]Nadia Baaziz, Omar Abahmane, Rokia Missaoui “Texture feature extraction in the spatial-frequency domain for content-based image retrieval”, eprint arXiv:1012.5208, arXiv.org - cs - arXiv:1012.5208.

[8]Ramadass Sudhir , Lt. Dr. S. Santhosh Baboo “An Efficient CBIR Technique with YUV Color Space and Texture Features”, Computer Engineering and Intelligent Systems, ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online) www.iiste.org, Vol 2, No.6, 2011.

[9]Neetu Sharma., Paresh Rawat, and jaikaran Singh, “Efficient CBIR Using Color Histogram Processing, Signal & Image Processing”, An International Journal(SIPIJ) Vol.2, No.1, March 2011.

[10]A Vadivel, A K Majumdar, Shamik Sural, “Perceptually Smooth Histogram Generation from the HSV Color Space for Content Based Image Retrieval”, Int. Conf. on Advances in Pattern Recognition (ICAPR2003), Calcutta, India, 248-251, 2003.

[11]Yixin Chen, James Z. Wang, “CLUE: Cluster-Based Retrieval of Images by Unsupervised Learning”, IEEE Transactions on Image Processing, Vol. 14, No. 8, August 2005.

[12]H.Yu, M. Li, H J. Zhang and J. Feng, “Color Texture Moments for Content-Based Image Retrieval”, Proc. Int. Conference on Image Processing, Volume III, 929-931, 2002.

[13]Gwangwon Kang, Junguk Beak, “Features Defined by Median Filtering on RGB Segments for Image Retrieval”, Second UKSIM European Symposium on Computer Modeling and Simulation, 978-0-7695-3325-4/08 $25.00 © 2008 IEEE, DOI 10.1109/EMS.2008.105.

[14]M. J. Swain and D. H.Ballard. Color indexing. International Journal of Computer Vision, 7(1):11 32, 1991.

[15]P.S.Suhasini , Dr. K.Sri Rama Krishna “CBIR Using Color Histogram Processing” , Journal of Theoretical and Applied Information Technology, 2005 - 2009 JATIT.

[16]Nam Yee Kim, Kang Soo You, Gi-Hyoung Yoo, Hoon Sung Kwak, “An Efficient Histogram Algorithm for Retrieval from Lighting Changed-Images”, Future Generation Communication and Networking Symposia, 2008. FGCNS '08. Second International Conference on (Volume:3 ), ISBN- 978-1-4244-3430-5, 13-15 Dec. 2008.

[17]Wang Database: http://wang.ist.psu.edu/docs/related/.

[18]Wei-Min Zheng, Zhe-Ming Lu, “Color Image Retrieval Schemes Using Index Histograms Based On Various Spatial-Domain Vector Quantizers”, International Journal of Innovative Computing, Information and Control ICIC, 2006 ISSN 1349-4198 Volume 2, Number 6, December.

[19]H. B. Kekre, Ms. Kavita Sonawane “Linear Equation in Parts as Histogram Specification for CBIR Using Bins Approach”, International Journal of Engineering Research and Development e-ISSN: 2278-067X, p-ISSN: 2278-800X, www.ijerd.com Volume 4, Issue 4 (October 2012), PP. 73-85.

[20]H. B. Kekre, Ms. Kavita Sonawane “Histogram Partitioning for Feature Vector Dimension Reduction in Bins Approach for CBIR”, International Journal of Electronics Communication and Computer Engineering Volume 3, Issue 6, ISSN (Online): 2249–071X, ISSN (Print): 2278–4209 PNO 1422.

[21]Simone Santini, Ramesh Jain, “Similarity Measures” IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 21, No. 9, September 1999.

[22]John P, Van De Geer, “Some Aspects of Minkowski distance”, Department of data theory, Leiden University. RR-95-03.

[23]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.

[24]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.

[25]Dengsheng Zhang and Guojun Lu “Evaluation Of Similarity Measurement for Image Retrieval” www. Gscit.monash.edu.au/~dengs/resource/papers/icnnsp03.

[27]Thomas Deselaers, Daniel Keysers, and Hermann Ney, “Classification Error Rate for Quantitative Evaluation of Content-basedImage Retrieval Systems”, http://www.robots.ox.ac.uk/˜vgg/data.

[28]Danzhou Liu, Member, Kien A. Hua, Khanh Vu, “Fast Query Point Movement Techniques for Large CBIR Systems” IEEE Transactions On Knowledge And Data Engineering, VOL. 21, NO. 5, MAY 2009.

[29]Tanusree Bhattacharjee, Biplab Banerjee, Nirmalya Chowdhury, “An Interactive Content Based Image Retrieval Technique and Evaluation of its Performance in High Dimensional and Low Dimensional Space”, International Journal of Image Processing (IJIP), Volume(4) : Issue(4).

International Journal of Image, Graphics and Signal Processing (IJIGSP)