Sparse Representation Based Histogram in Color Texture Retrieval

Cong Bai, Jianan Chen, Jinglin Zhang, Kidiyo Kpalma, Joseph Ronsin

Abstract: Sparse representation is proposed to generate the histogram of feature vectors, namely sparse representation based histogram (SRBH), in which a feature vector is represented by a number of basis vectors instead of by one basis vector in classical histogram. This amelioration makes the SRBH to be a more accurate representation of feature vectors, which is confirmed by the analysis in the aspect of reconstruction errors and the application in color texture retrieval. In color texture retrieval, feature vectors are constructed directly from coefficients of Discrete Wavelet Transform (DWT). Dictionaries for sparse representation are generated by K-means. A set of sparse representation based histograms from different feature vectors is used for image retrieval and chi-squared distance is adopted for similarity measure. Experimental results assessed by Precision-Recall and Average Retrieval Rate (ARR) on four widely used natural color texture databases show that this approach is robust to the number of wavelet decomposition levels and outperforms classical histogram and state-of-the-art approaches.