CBIR based on adaptive segmentation of hsv color space

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Referenz

Youngeun An; Riaz, M.; Jongan Park: CBIR based on adaptive segmentation of hsv color space. Computer Modelling and Simulation (UKSim), 2010 12th International Conference on, pp.248-251, 24-26 March 2010

DOI

http://dx.doi.org/10.1109/UKSIM.2010.53

Abstract

Proposed algorithm is based on color information using HSV color space. Histogram search characterizes an image by its color distribution, or histogram but the drawback of a global histogram representation is that information about object location, shape, and texture is discarded. Thus local histogram is used for extracting the maximum color occurrence from each segment. Before extracting the maximum color from each segment the input image is adaptively segmented. Different quantization of hue and saturation are used for partitioning the image into different number of segments. Finally minkowski metric is used for feature vector comparison. Web based image retrieval demo system is built to make it easy to test the retrieval performance and to expedite further algorithm investigation.

Extended Abstract

Bibtex

@INPROCEEDINGS{5481172,
author={Y. An and M. Riaz and J. Park},
booktitle={Computer Modelling and Simulation (UKSim), 2010 12th International Conference on},
title={CBIR Based on Adaptive Segmentation of HSV Color Space},
year={2010},
pages={248-251},
keywords={content-based retrieval;feature extraction;image colour analysis;image retrieval;image segmentation;image texture;quantisation (signal);HSV color space adaptive segmentation;Web based image retrieval demo system;color distribution;content based image retrieval;global histogram representation;histogram distribution;hue quantization;image partitioning;image segmentation;image texture;local histogram;maximum color occurrence extraction;minkowski metric;saturation quantization;Data mining;Feature extraction;Histograms;Humans;Image databases;Image processing;Image retrieval;Image segmentation;Information retrieval;Spatial databases;Adaptive Segmentation;Feature Extraction;HSV Color Space;Image Retrieval;Maximum Color},
doi={10.1109/UKSIM.2010.53},
url={  },
month={March},
}

Used References

M. J. Swain and D. H. Ballard, "Color Indexing", International Journal of Computer Vision Vol.7 No.1, pp.11-32, 1991. http://dx.doi.org/10.1007/BF00130487

B. V. Funtand G.D. Finlayson, "Color constant color indexing ", IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 17, pp.522-529, 1995.

B. M. Mehtre, M. S. Kankanhalli, A. D. Narsimhalu and G. C. Man, "Color matching for image retrieval", Pattern Recognition Letter, Vol.16, pp.325-331, 1995. http://dx.doi.org/10.1016/0167-8655(94)00096-L

M. Safar, C. Shahabi and X. Sun, "Image retrieval by shape: a comparative study", ICME 2000, Vol.1, pp.141-144, 2000.

A.K. Jain, "Fundamental of Digital Image Processing", Prentice Hall International, 1989.

A. Khotanzad and Y.H. Hong, "Invariants Image Recognition by Zernike Moments", IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.12, No.5, pp.489-497, 1990. http://dx.doi.org/10.1109/34.55109

S. Belongie, C. Carson, H. Greenspan and J. Malik, "Color and Texture-based Image Segmentation Using EM and Its Application to Content-based Image Retrieval", Computer Science Division, University of California at Berkeley, ICCV'98, 1998.

R. Chellappa and S. Chatterjee, "Classification of Textures Using Gaussian Markov Random Fields", IEEE Trans. on Acoustics, Speech, and Signal Processing, vol.ASSP-33, no.4, pp.959, 1985. http://dx.doi.org/10.1109/TASSP.1985.1164641

B. S. Manjunath and W. Y. Ma, "Texture Features for Browsing and Retrieval of Image Data", IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.18, No.8, pp.837-842, 1996. http://dx.doi.org/10.1109/34.531803

J. Huang, "Color-Spactial Image Indexing and Application", Ph.D. thesis in Cornell Univ., 1998.

G. Pass and R. Zabih, "Comparing images using joint histogram", Multimedia Systems, Vol.7, pp.234-240, 1999. http://dx.doi.org/10.1007/s005300050125

Muhammad Riaz, Gwangwon Kang, Youngbae Kim, Sungbum Pan, and Jongan Park "Efficient Image Retrieval Using Adaptive Segmentation of HSV Color Space", International Conference on Computational Sciences and Its Applications, 2008. ICCSA '08. June 30-July 3 2008.

Jongan Park, Sungkwan Kang, Ilhoe Jeong, Waqas Rasheed, Seungjin Park and Youngeun, "Web Based Image Retrieval System Using HSI Color Indexes", Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007.

J. Chen, T.N. Pappas, A. Mojsilovic,and B. E. Rogowitz, "Adaptive image segmentation based on color and texture", Proc. ICIP 2002, Rochester, New York, Sept. 2002.

David A. Forsyth, Jean Ponce, "Computer Vision: A Modern Approach", Prentice Hall; US Ed edition (August 24, 2002), page 53-91, 2002.

Bo Di, "An efficient image retrieval approach base on color clustering", Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2007. IIHMSP 2007.

Jia Li, James Z. Wang, "Automatic linguistic indexing of pictures by a statistical modeling approach," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1075-1088, 2003. http://dx.doi.org/10.1109/TPAMI.2003.1227984

James Z. Wang, Jia Li, Gio Wiederhold, "SIMPLIcity: Semanticssensitive Integrated Matching for Picture LIbraries," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol 23, no.9, pp. 947-963, 2001. http://dx.doi.org/10.1109/34.955109

James Z. Wang, "Integrated Region-Based Image Retrieval", Boston, Kluwer Academic Publishers, 2001. http://dx.doi.org/10.1007/978-1-4615-1641-5

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