Automatic Feature Weight Assignment Based on Image Retrieval Using Genetic Algorithm

Article Preview

Abstract:

The work proposes the new method to increase an efficiency of a Content-based Image Retrieval (CBIR) system. For combining many image features, the optimal weight of each feature is required. To find the optimal value of the feature, this work uses Genetic Algorithm (GA). An image is represented as color, shape and texture features. The experiment compares the results from the system with equal weight values and the system with the weights provided by GA. Evaluation shows the robustness and efficiency of the proposed technique.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 931-932)

Pages:

1402-1406

Citation:

Online since:

May 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] M. Swain, D. Ballad, Color indexing, International Journal of Computer Vision, 7(1) (1991) 11-32.

Google Scholar

[2] X. Wang, K. Xie, Content-based image retrieval incorporating the AHP method, International Journal of Information Technology, 11(1) (2005) 25-37.

Google Scholar

[3] M. Stricker, M. Orengo, Similarity of color images, Proceeding of the SPIE conference, 2420 (1995) 381-392.

Google Scholar

[4] M. Hu, Visual pattern recognition by moment invariants, IRE Transactions on Information Theory, 8(2) (1962) 179-187.

DOI: 10.1109/tit.1962.1057692

Google Scholar

[5] R. Haralick, K. Shanmugam, I. Dinstein, Textural features for image classification, IEEE transactions on systems, man, and cybernetics, 3(6) (1973) 610-621.

DOI: 10.1109/tsmc.1973.4309314

Google Scholar

[6] P. S. Hiremath, J. Pujari, Content based image retrieval based on color, texture and shape features using Image and its complement, International Journal of Computer Science and Security, 1(4) (2007) 25-35.

DOI: 10.1109/adcom.2007.21

Google Scholar

[7] C. Liu, Z. Wei, Multi-feature Method: An Integrated Content Based Image Retrieval System, 2011 2nd International Symposium on Intelligence Information Processing and Trusted Computing (IPTC), (2011) 43-46.

DOI: 10.1109/iptc.2011.18

Google Scholar

[8] H. Shao, J. Zhang, W. Cui, H. Zhao, Automatic feature weight assignment based on genetic algorithm for image retrieval, Proceedings of 2003 IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, 2 (2003) 731-735.

DOI: 10.1109/rissp.2003.1285675

Google Scholar

[9] R. Gali, M.L. Dewal, R.S. Anand, Genetic Algorithm for Content Based Image Retrieval, 2012 Fourth International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN), (2012) 243-247.

DOI: 10.1109/cicsyn.2012.52

Google Scholar

[10] M. Chen, P. Fu, Y. Sun, H. Zhang, Image retrieval based on multi-feature similarity score fusion using genetic algorithm, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), 2 (2010) 45-49.

DOI: 10.1109/iccae.2010.5451373

Google Scholar

[11] F. Herrera, M. Lozano, A. M. Sa´ nchez, A Taxonomy for the Crossover Operator for Real-Coded Genetic Algorithms: An Experimental Study, International Journal of Intelligent Systems, 18 (2003) 309–338.

DOI: 10.1002/int.10091

Google Scholar

[12] Information on http: /wang. ist. psu. edu/docs/related. shtml.

Google Scholar