Discriminative Regional Color Co-Occurrence Descriptor

Traditional color feature descriptors are focused on color-value distributions in the color space, e.g., color histograms, color bag-of-words, which ignore the spatial location and contextual information of different colors. In this paper, a new regional color co-occurrence feature descriptor (RCC) is proposed to reflect spatial relations of colors in an image. First, we partition an image into a number of disjoint regions using superpixel techniques. Then, we construct a color histogram for each region, based on which we construct a color co-occurrence matrix for each pair of neighboring regions. Finally, all the constructed co-occurrence matrices from an image are summed up and normalized as a color descriptor to represent this image. This new color descriptor reflects the color-collocation patterns in the image. We use this new color descriptor for image/object classification and find that it leads to higher classification accuracies than other competing color descriptors.