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Background Modeling Using Temporal-Local Sample Density Outlier Detection Read chapter Read first chapter

2017 | OriginalPaper | Chapter

A Novel Visual Word Assignment Model for Content-Based Image Retrieval

Authors : Anindita Mukherjee, Soman Chakraborty, Jaya Sil, Ananda S. Chowdhury

Published in: Proceedings of International Conference on Computer Vision and Image Processing

Publisher: Springer Singapore

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Abstract

Visual bag of words model have been applied in the recent past for the purpose of content-based image retrieval. In this paper, we propose a novel assignment model of visual words for representing an image patch. In particular, a vector is used to represent an image patch with its elements denoting the affinities of the patch to belong to a set of closest/most influential visual words. We also introduce a dissimilarity measure, consisting of two terms, for comparing a pair of image patches. The first term captures the difference in affinities of the patches to belong to the common set of influential visual words. The second term checks the number of visual words which influences only one of the two patches and penalizes the measure accordingly. Experimental results on the publicly available COIL-100 image database clearly demonstrates the superior performance of the proposed content-based image retrieval (CBIR) method over some similar existing approaches.

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Literature
1.
Datta, R., Joshi, D., Li, J. and Wang, James Z., Image retrieval: Ideas, influences, and trends of the new age, ACM Comput. Surv., 40(2), 1–60, (2008).
2.
3.
Liu G. and Yang J.: Content-based image retrieval using color difference histogram, Pattern Recognition, 46(1), 188–198, (2013).
4.
Hejazi M. R. and Ho Y.: An efficient approach to texture-based image retrieval, Int. J. Imaging Systems and Technology, 17(5), 295–302, (2007).
5.
Santini S. and Jain R.: Similarity Measures, IEEE Trans. Pattern Anal. Mach. Intell., 21(9), 871–883, (1999).
6.
Goldberger J., Gordon S., Greenspan H.: An Efficient Image Similarity Measure Based on Approximations of KL-Divergence Between Two Gaussian Mixtures, 9th IEEE International Conference on Computer Vision (ICCV), 487–493, (2003).
7.
Hu W., Xie N., Li, L., Zeng, X., Maybank, S. J.: A Survey on Visual Content-Based Video Indexing and Retrieval, IEEE Trans. Systems, Man, and Cybernetics, Part C, 41(6), 797–819, (2011).
8.
Lowe D. G.: Distinctive Image Features from Scale-Invariant Keypoints, Int. J. Computer Vis., 60(2), 91–110, (2004).
9.
Sivic, J., Zisserman, A.: Video Google: Efficient Visual Search of Videos, In Toward Category-Level Object Recognition, 127–144, (2006).
10.
Squire, D. M., Wolfgang, M., Henning, M., Thierry P.: Content-based query of image databases: inspirations from text retrieval, Pattern Recognition Lett., 21(13-14), 1193–1198, (2000).
11.
Yang, J., Jiang, Y. G., Hauptmann, A. G., Ngo, C. W.: Evaluating bag-of-visual-words representations in scene classification, Proceedings of the 9th ACM SIGMM International Workshop on Multimedia Information Retrieval (MIR), 197–206, (2007).
12.
Kato, H., Tatsuya H.: Image Reconstruction from Bag-of-Visual-Words, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 955–962, (2014).
13.
Bouachir, W., Kardouchi, M., Belacel, N.: Improving Bag of Visual Words Image Retrieval: A Fuzzy Weighting Scheme for Efficient Indexation, Fifth International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 215–220, (2009).
14.
Kannan, S. R., Ramathilagam, S., Chung, P. C.: Effective fuzzy c-means clustering algorithms for data clustering problems, Expert Syst. Appl., 39(7), 6292–6300, (2012).
15.
Sivic, J., Zisserman A.: Video Google: A Text Retrieval Approach to Object Matching in Videos, 9th IEEE International Conference on Computer Vision (ICCV), 1470–1477,(2003).
16.
Nene, S. A., Nayar, S. K., Murase, H.: Columbia Object Image Library (COIL-100), Technical Report, Department of Computer Science, Columbia University CUCS-006-96, (1996).
17.
Newsam, S., Yang Y.: Comparing global and interest point descriptors for similarity retrieval in remote sensed imagery, 15th ACM International Symposium on Geographic Information Systems (ACM-GIS), 1–9, (2007).
18.
19.
Dimitrovski I., Kocev D., Loskovska S., Dzeroski S.: Improving bag-of-visual-words image retrieval with predictive clustering trees, Information Sciences, 329, 851–865, (2016).
Metadata
Title
A Novel Visual Word Assignment Model for Content-Based Image Retrieval
Authors
Anindita Mukherjee
Soman Chakraborty
Jaya Sil
Ananda S. Chowdhury
Copyright Year
2017
Publisher
Springer Singapore
Book
Proceedings of International Conference on Computer Vision and Image Processing

Print ISBN: 978-981-10-2103-9

Electronic ISBN: 978-981-10-2104-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-2104-6_8