A New Relevance Feedback based Approach for Efficient Image Retrieval
|
10.5120/9993-4846 |
Karthik S, Snehanshu Saha and Chaithra G. Article: A New Relevance Feedback based Approach for Efficient Image Retrieval. International Journal of Computer Applications 61(14):1-6, January 2013. Full text available. BibTeX
@article{key:article,
author = {Karthik S and Snehanshu Saha and Chaithra G},
title = {Article: A New Relevance Feedback based Approach for Efficient Image Retrieval},
journal = {International Journal of Computer Applications},
year = {2013},
volume = {61},
number = {14},
pages = {1-6},
month = {January},
note = {Full text available}
}
Abstract
The rapid growth of digital image data summons the need for an effective and efficient content-based image searching system. Such systems should address the needs of the end user and should deliver the relevant images based on the search criteria. In order to meet this requirement, the content-based image search technique should capture the color and texture information. The performance of the algorithm can be enhanced using relevance feedback method. In this paper, a content-based image retrieval method based on image and its complement is presented. The similarity between the images is identified using an approach based on most significant highest priority (MSHP) principle or using a new distance measure which belongs to minkowski family. The retrieval rate is enhanced by relevance feedback technique based on k-means algorithm. The approach is tested on Simplicity test dataset and a comparable performance was achieved
References
- P Kshirsagar, V Munde, S Deshpande, " Semantic Based Search technology for Images", International conference and workshop on emerging trends in Technology, Maharastra, Feb 26-27, 2010, pp 550-553
- Fabio F Faria, Adriano Veloso, Humberto Almeida, Eduardo Valle, Ricardo S Torres, Marcos A Goncalves, " Learning to Rank for Content Based Image Retrieval", MIR-2010, Pennsylvania, March 29-31, 2010, pp 285-294
- Giorgio Giacinto, " A Nearest Neighbor Approach to Relevance Feedback in Content based image retrieval", CIVR-2007, July 9-11, 2007, pp 456-463
- Wei Bian, Dacheng Tao, " Biased Discriminant Euclidean Embedding for Content based image retrieval", IEEE transactions on image processing, Vol 19, No 2, February 2010, pp 545-554
- Kien A Hua, Khanh Vu, Jung-Hwan Oh, " SamMatch: A flexible and efficient sampling based image retrieval technique for large image databases", ACM transaction on Multimedia, 1999, pp 225-234
- Juan C Caicedo, Fabio A G, Edwin Triana, Eduardo Romero, " Design of a Medical image database with content based retrieval capabilities", PSIVT 2007, pp 919-931
- Mei-Ling Shyu, Shu-Ching Chen, Chengcui Zhang, " A unified framework for image database clustering and content based retrieval", MMDB-04, November 13,2004, pp 19-27
- Feng Jing, Bo Zhang, Fuzong Lin, Wei-Ying ma, " A novel region based image retrieval method using relevance feedback", International conference on Multimedia information retrieval, 2001, pp 28-31
- P. S. Hiremath, Jagadeesh Pujari, "Content Based Image Retrieval based on Color, Texture and Shape features using Image and its complement", International Journal of Computer Science and Security, Volume 1, Issue 4, 2007,pp. 25-35
- Y. Rubner, L. J. Guibas, and C. Tomasi, "The earth mover's distance, multi-dimensional scaling, and color-based image retrieval", Proceedings of DARPA Image understanding Workshop, 1997, pp. 661-668
- J. Li, J. Z. Wang, and G. Wiederhold, "IRM: Integrated Region Matching for Image Retrieval," Proc. of the 8th ACM International Conference on Multimedia, 2000, pp. 147-156.
- M. Banerjee, M,K,Kundu and P. K. Das, "Image Retrieval with Visually Prominent Features using Fuzzy set theoretic Evaluation", ICVGIP, 2004.
- Courant,R, D. Hilbert, " Methods of Mathematical Physics", Interscience/Wiley publication, 1989
- Erwin Kreyszig, " Introductory Functional Analysis with applications", Wiley publications, 1978