A DATA-DRIVEN COLOR FEATURE LEARNING SCHEME FOR IMAGE RETRIEVAL

This paper addresses content based image retrieval based on color features. Several previous works have addressed color based image retrieval based on hand-crafted features. In this paper, a data-driven learning framework is proposed for generating color based signatures. To obtain the features, a linear transformation is learned from the pixel values based on its reconstruction error. Using this linear transformation, the original pixel values are transformed into a higher dimensional space. In the higher dimensional space, a dictionary is learned to obtain the sparse codes of the pixels. A max pooling strategy is used to obtain the dominant color features of a region and the final feature vector for an image is obtained by concatenating the pooled features. We evaluate our approach following the standard evaluation criteria for the INRIA Holidays and University of Kentucky Benchmark datasets. The approach is compared with several baselines such as histograms in RGB, HSV, YUV and Lab color spaces and several other color based features proposed for addressing this problem. Our approach shows competitive results on these datasets and outperforms all the baselines.