Image retrieval using combination of color and multiresolution texture features
Young Deok Chun, Joong Ki Sung, Nam Chul Kim · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
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.