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Proceedings Paper

Image retrieval using combination of color and multiresolution texture features
Author(s): Young Deok Chun; Joong Ki Sung; Nam Chul Kim
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Paper Abstract

We propose a content-based image retrieval (CBIR) method based on an efficient combination of a color feature and multiresolution texture features. As a color feature, a HSV autocorrelogram is chosen which is known to measure spatial correlation of colors well. As texture features, BDIP and BVLC moments are chosen which is known to measure local intensity variations well and measure local texture smoothness well, respectively. The texture features are obtained in a wavelet pyramid of the luminance component of a color image. The extracted features are combined for efficient similarity computation by the normalization depending on their dimensions and standard deviation vectors. Experimental results show that the proposed method yielded average 10% better performance in precision vs. recall and average 0.12 in average normalized modified retrieval rank (ANMRR) than the methods using color autocorrelogram, BDIP and BVLC moments, and wavelet moments, respectively.

Paper Details

Date Published: 17 January 2005
PDF: 9 pages
Proc. SPIE 5682, Storage and Retrieval Methods and Applications for Multimedia 2005, (17 January 2005); doi: 10.1117/12.586301
Show Author Affiliations
Young Deok Chun, Kyungpook National Univ. (South Korea)
Joong Ki Sung, Kyungpook National Univ. (South Korea)
Nam Chul Kim, Kyungpook National Univ. (South Korea)


Published in SPIE Proceedings Vol. 5682:
Storage and Retrieval Methods and Applications for Multimedia 2005
Rainer W. Lienhart; Noboru Babaguchi; Edward Y. Chang, Editor(s)

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