Color Space Comparison between RGB and HSV Based Images Retrieval

Article Preview

Abstract:

The visual attributes of color are suitable for human perception and computer vision. A Color space is defined as a model for representing the intensity value of color. We propose a color space comparison and analysis between RGB and HSV based images retrieval. We succeed in transferring the image retrieval problem to sequences comparison and subsequently using the color sequences comparison between the color featurs of RGB and HSV to compare and analyze the images of database.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

4123-4126

Citation:

Online since:

July 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] P. Aigrain, H. J. Zhang, and D. Petkovic, Content-based representation and retrieval of visual media: A state-of-the-art review, Multimedia Tools and Applications, 3: 179–202, (1996).

DOI: 10.1007/bf00393937

Google Scholar

[2] Sameer Antani, Rangachar Kasturi, Ramesh Jain, A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video, Pattern Recognition Volume: 35, Issue: 4, April, 2002, pp.945-965.

DOI: 10.1016/s0031-3203(01)00086-3

Google Scholar

[3] Torres, R. da S.; Falcão, A.X., Contour salience descriptors for effective image retrieval and analysis , Image and Vision Computing Volume: 25, Issue: 1, January, 2007, pp.3-13.

DOI: 10.1016/j.imavis.2005.12.010

Google Scholar

[4] Sameer Antani, Rangachar Kasturi, Ramesh Jain, A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video, Pattern Recognition Volume: 35, Issue: 4, April, 2002, pp.945-965.

DOI: 10.1016/s0031-3203(01)00086-3

Google Scholar

[5] James Z. Wang, Jia Li, Gio Wiederhold, ``SIMPLIcity: Semantics-sensitive Integrated Matching for Picture LIbraries, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol 23, no. 9, pp.947-963, (2001).

DOI: 10.1109/34.955109

Google Scholar

[6] Chiunhsiun Lin, Ching-Hung Su, Hsuan Shu Huang, and Kuo-Chin Fan MLB Sports Frames Retrieval Using Color Cipher Similarities, INTERNATIONAL JOURNAL OF CIRCUITS, SYSTEMS AND SIGNAL PROCESSING Issue 6, Volume 5, pp.565-580, (2011).

Google Scholar

[7] J. R. Smith, Integrated spatial and feature image system: Retrieval, analysis and compression , PhD dissertation, Columbia University, New York, (1997).

Google Scholar

[8] W. Rasheed , G. Kang, J. Kang, J. Chun and J. Park, Sum of Values of Local Histograms for Image Retrieval, in Proc. International Conference on Networked Computing and Advanced Information Management, vol. 2, pp.690-694, (2008).

DOI: 10.1109/ncm.2008.91

Google Scholar

[9] R. Datta, D. Joshi, J. Li, J.Z. Wang, Image retrieval: ideas, influences, and trends of the new age , ACM Computing Surveys 40(2), 2008, pp.1-60.

DOI: 10.1145/1348246.1348248

Google Scholar

[10] V.N. Gudivada, and V.V. Raghavan, Content based image retrieval systems, IEEE Computer, Vol. 28, No. 9, 1995, pp.18-22.

DOI: 10.1109/2.410145

Google Scholar