Content-Based Image Retrieval Using Color Volume Histograms

Human visual perception has a close relationship with the HSV color space, which can be represented as a cylinder. The question of how visual features are extracted using such an attribute is important. In this paper, a new feature descriptor; namely, a color volume histogram, is proposed for image representation and content-based image retrieval. It converts a color image from RGB color space to HSV color space and then uniformly quantizes it into 72 bins of color cues and 32 bins of edge cues. Finally, color volumes are used to represent the image content. The proposed algorithm is extensively tested on two Corel datasets containing 15 000 natural images. These image retrieval experiments show that the color volume histogram has the power to describe color, texture, shape and spatial features and performs significantly better than the local binary pattern histogram and multi-texton histogram approaches.