Grouping and Indexing Color Features for Efficient Image Retrieval
Content-based Image Retrieval (CBIR) aims at searching image databases for specific images that are similar to a given query image based on matching of features derived from the image content. This paper focuses on a low-dimensional color based indexing technique for achieving efficient and effective retrieval performance. In our approach, the color features are extracted using the mean shift algorithm, a robust clustering technique. Then the cluster (region) mode is used as representative of the image in 3-D color space. The feature descriptor consists of the representative color of a region and is indexed using a spatial indexing method that uses *R -tree thus avoiding the high-dimensional indexing problems associated with the traditional color histogram. Alternatively, the images in the database are clustered based on region feature similarity using Euclidian distance. Only representative (centroids) features of these clusters are indexed using *R -tree thus improving the efficiency. For similarity retrieval, each representative color in the query image or region is used independently to find regions containing that color. The results of these methods are compared. A JAVA based query engine supporting query-by- example is built to retrieve images by color.
[1] M. A. Stricker and M. Orengo, Similarity of color images, Proc. SPIE,
Storage Retrieval Still Image Video Databases IV, vol. 2420, pp. 381-
392, 1996.
[2] M. Stricker and A. Dimai, Color indexing with weak spatial constraints,
Proc. SPIE Storage Retrieval Still Image Video Databases IV, vol. 2670,
pp. 29-40,1996.
[3] J. Smith and S.-F. Chang, Tools and techniques for color image retrieval,
Proc. SPIE, vol. 2670, pp. 2-7, 1996.
[4] G. Pass and R. Zabih, Histogram refinement for content based image
retrieval, Proc. IEEE Workshop Applications Computer Vision, pp. 96-
102, 1996.
[5] J.Huang, S R Kumar, M Mithra, W.Zhu, and R. Zabih, Image indexing
using color correlograms, Proc. IEEE conf. Computer vision and pattern
Recognition, pp. 762-768,1997.
[6] H. Zhang, Y. Gong, C. Y. Low, and S.W. Smoliar, Image retrieval based
on color features: An evaluation study, Proc. SPIE Digital Image
Storage Archiving Systems, vol. 2606, pp. 212-220,1995.
[7] X. Wan,C. J. Kuo, A multiresolution color clustering approach to image
indexing and retrieval, Proc. IEEE Int. Conf. Acoustics, Speech, Signals
Processing, vol. 6, pp. 3705-3708,1998.
[8] J. Hafner, H. S. Sawhney, W. Equitz, M. Flickner, and W. Niblack,
Efficient color histogram indexing for quadratic form distance functions,
IEEE Trans. Pattern Anal. Machine Intell., vol. 17, pp. 729-736, July
1995.
[9] G. Cha and C. Chung, Multi-mode indices for effective image retrieval
in multimedia systems, Proc. IEEE Multimedia Computing Systems, pp.
152-159, 1998.
[10] W. Y. Ma and H. Zhang, Benchmarking of image features for contentbased
retrieval, Proc. IEEE 32nd Asilomar Conf. Signals, Systems,
Computers, vol. 1, pp. 253-257,1998.
[11] J. R. Smith, C. S. Li, Image classification and querying using composite
region templates, Journal of Computer Vision and Image
Understanding, vol. 23, 2001.
[12] A. Gupta, R. Jain, Visual information retrieval, Communications of the
ACM, vol. 40, no. 5, pp. 70-79, May 1997.
[13] S. Mukherjea, K. Hirata, Y. Hara, AMORE: a World Wide Web image
retrieval engine, World Wide Web, vol. 2, no. 3, pp. 115-32, Baltzer,
1999.
[14] Natsev, R. Rastogi, K. Shim, WALRUS: A similarity retrieval algorithm
for image databases, Proc. ACM SIGMOD, Philadelphia, PA, 1999.
[15] Pentland, R. W. Picard, S. Sclaro, Photobook: tools for content-based
manipulation of image databases, Proc. SPIE, vol. 2185, pp. 34-47, San
Jose, February 7-8, 1994.
[16] R. W. Picard, T. Kabir, Finding similar patterns in large image
databases, Proc. IEEE ICASSP, Minneapolis, vol. V, pp. 161-64, 1993.
[17] C. Carson, M. Thomas, S. Belongie, J. M. Hellerstein, J. Malik,
Blobworld: a system for region-based image indexing and retrieval,
Proc. Int. Conf. on Visual Information Systems, D. P. Huijsmans, A.
W.M. Smeulders (eds.), Springer, Amsterdam, The Netherlands, June 2-
4, 1999.
[18] S. Stevens, M. Christel, H. Wactlar, Informedia: improving access to
digital video, Interactions, vol. 1, no. 4, pp. 67-71, 1994.
[19] S. Mehrotra, Y. Rui, M. Ortega-Binderberger, T.S. Huang, Supporting
content-based queries over images in MARS, Proc. IEEE International
Conference on Multimedia Computing and Systems, pp. 632-3, Ottawa,
Ont., Canada 3-6 June 1997.
[20] W. Y. Ma, B. Manjunath, NeTra: A toolbox for navigating large image
databases," Proc. IEEE Int. Conf. Image Processing, pp. 568-71, 1997.
[21] R. Jain, S. N. J. Murthy, P. L.-J. Chen, S. Chatterjee, Similarity
measures for image databases, Proc. SPIE, vol. 2420, pp. 58-65, San
Jose, CA, Feb. 9-10, 1995.
[22] J. Z. Wang, G. Wiederhold, O. Firschein, X. W. Sha, Content-based
image indexing and searching using Daubechies' wavelets, International
Journal of Digital Libraries, vol. 1, no. 4, pp. 311-328, 1998.
[23] J. Z. Wang, Integrated Region-Based Image Retrieval, Kluwer
Academic Publishers, 190 pp., 2001.
[24] Zaher aghbari, Akifumi makinouchi, Semantic Approach to Image
Database Classification and Retrieval, NII Journal No. 7, 2003.
[25] Shu-Ching, Chen Stuart H. Rubin, Mei-Ling, A Dynamic User Concept
Pattern Learning,Framework for Content-Based Image Retrieval , IEEE
transactions on systems, man, and cyberneticsÔÇöpart c: applications and
reviews, vol. 36, no. 6, November 2006.
[26] M. V. Sudhamani, C.R. Venugopal, Non-parametric classification of
image data through clustering: An application for image Retrieval, Proc.
of IEEE Int.Conf. Image and signal processing, Dec 2006.
[1] M. A. Stricker and M. Orengo, Similarity of color images, Proc. SPIE,
Storage Retrieval Still Image Video Databases IV, vol. 2420, pp. 381-
392, 1996.
[2] M. Stricker and A. Dimai, Color indexing with weak spatial constraints,
Proc. SPIE Storage Retrieval Still Image Video Databases IV, vol. 2670,
pp. 29-40,1996.
[3] J. Smith and S.-F. Chang, Tools and techniques for color image retrieval,
Proc. SPIE, vol. 2670, pp. 2-7, 1996.
[4] G. Pass and R. Zabih, Histogram refinement for content based image
retrieval, Proc. IEEE Workshop Applications Computer Vision, pp. 96-
102, 1996.
[5] J.Huang, S R Kumar, M Mithra, W.Zhu, and R. Zabih, Image indexing
using color correlograms, Proc. IEEE conf. Computer vision and pattern
Recognition, pp. 762-768,1997.
[6] H. Zhang, Y. Gong, C. Y. Low, and S.W. Smoliar, Image retrieval based
on color features: An evaluation study, Proc. SPIE Digital Image
Storage Archiving Systems, vol. 2606, pp. 212-220,1995.
[7] X. Wan,C. J. Kuo, A multiresolution color clustering approach to image
indexing and retrieval, Proc. IEEE Int. Conf. Acoustics, Speech, Signals
Processing, vol. 6, pp. 3705-3708,1998.
[8] J. Hafner, H. S. Sawhney, W. Equitz, M. Flickner, and W. Niblack,
Efficient color histogram indexing for quadratic form distance functions,
IEEE Trans. Pattern Anal. Machine Intell., vol. 17, pp. 729-736, July
1995.
[9] G. Cha and C. Chung, Multi-mode indices for effective image retrieval
in multimedia systems, Proc. IEEE Multimedia Computing Systems, pp.
152-159, 1998.
[10] W. Y. Ma and H. Zhang, Benchmarking of image features for contentbased
retrieval, Proc. IEEE 32nd Asilomar Conf. Signals, Systems,
Computers, vol. 1, pp. 253-257,1998.
[11] J. R. Smith, C. S. Li, Image classification and querying using composite
region templates, Journal of Computer Vision and Image
Understanding, vol. 23, 2001.
[12] A. Gupta, R. Jain, Visual information retrieval, Communications of the
ACM, vol. 40, no. 5, pp. 70-79, May 1997.
[13] S. Mukherjea, K. Hirata, Y. Hara, AMORE: a World Wide Web image
retrieval engine, World Wide Web, vol. 2, no. 3, pp. 115-32, Baltzer,
1999.
[14] Natsev, R. Rastogi, K. Shim, WALRUS: A similarity retrieval algorithm
for image databases, Proc. ACM SIGMOD, Philadelphia, PA, 1999.
[15] Pentland, R. W. Picard, S. Sclaro, Photobook: tools for content-based
manipulation of image databases, Proc. SPIE, vol. 2185, pp. 34-47, San
Jose, February 7-8, 1994.
[16] R. W. Picard, T. Kabir, Finding similar patterns in large image
databases, Proc. IEEE ICASSP, Minneapolis, vol. V, pp. 161-64, 1993.
[17] C. Carson, M. Thomas, S. Belongie, J. M. Hellerstein, J. Malik,
Blobworld: a system for region-based image indexing and retrieval,
Proc. Int. Conf. on Visual Information Systems, D. P. Huijsmans, A.
W.M. Smeulders (eds.), Springer, Amsterdam, The Netherlands, June 2-
4, 1999.
[18] S. Stevens, M. Christel, H. Wactlar, Informedia: improving access to
digital video, Interactions, vol. 1, no. 4, pp. 67-71, 1994.
[19] S. Mehrotra, Y. Rui, M. Ortega-Binderberger, T.S. Huang, Supporting
content-based queries over images in MARS, Proc. IEEE International
Conference on Multimedia Computing and Systems, pp. 632-3, Ottawa,
Ont., Canada 3-6 June 1997.
[20] W. Y. Ma, B. Manjunath, NeTra: A toolbox for navigating large image
databases," Proc. IEEE Int. Conf. Image Processing, pp. 568-71, 1997.
[21] R. Jain, S. N. J. Murthy, P. L.-J. Chen, S. Chatterjee, Similarity
measures for image databases, Proc. SPIE, vol. 2420, pp. 58-65, San
Jose, CA, Feb. 9-10, 1995.
[22] J. Z. Wang, G. Wiederhold, O. Firschein, X. W. Sha, Content-based
image indexing and searching using Daubechies' wavelets, International
Journal of Digital Libraries, vol. 1, no. 4, pp. 311-328, 1998.
[23] J. Z. Wang, Integrated Region-Based Image Retrieval, Kluwer
Academic Publishers, 190 pp., 2001.
[24] Zaher aghbari, Akifumi makinouchi, Semantic Approach to Image
Database Classification and Retrieval, NII Journal No. 7, 2003.
[25] Shu-Ching, Chen Stuart H. Rubin, Mei-Ling, A Dynamic User Concept
Pattern Learning,Framework for Content-Based Image Retrieval , IEEE
transactions on systems, man, and cyberneticsÔÇöpart c: applications and
reviews, vol. 36, no. 6, November 2006.
[26] M. V. Sudhamani, C.R. Venugopal, Non-parametric classification of
image data through clustering: An application for image Retrieval, Proc.
of IEEE Int.Conf. Image and signal processing, Dec 2006.
@article{"International Journal of Information, Control and Computer Sciences:53349", author = "M. V. Sudhamani and C. R. Venugopal", title = "Grouping and Indexing Color Features for Efficient Image Retrieval ", abstract = "Content-based Image Retrieval (CBIR) aims at searching image databases for specific images that are similar to a given query image based on matching of features derived from the image content. This paper focuses on a low-dimensional color based indexing technique for achieving efficient and effective retrieval performance. In our approach, the color features are extracted using the mean shift algorithm, a robust clustering technique. Then the cluster (region) mode is used as representative of the image in 3-D color space. The feature descriptor consists of the representative color of a region and is indexed using a spatial indexing method that uses *R -tree thus avoiding the high-dimensional indexing problems associated with the traditional color histogram. Alternatively, the images in the database are clustered based on region feature similarity using Euclidian distance. Only representative (centroids) features of these clusters are indexed using *R -tree thus improving the efficiency. For similarity retrieval, each representative color in the query image or region is used independently to find regions containing that color. The results of these methods are compared. A JAVA based query engine supporting query-by- example is built to retrieve images by color.
", keywords = "Content-based, indexing, cluster, region.", volume = "1", number = "3", pages = "524-6", }
