Content-based image retrieval using color difference histogram

This paper presents a novel image feature representation method, namely color difference histograms (CDH), for image retrieval. This method is entirely different from the existing histograms; most of the existing histogram techniques merely count the number or frequency of pixels. However, the unique characteristic of CDHs is that they count the perceptually uniform color difference between two points under different backgrounds with regard to colors and edge orientations in L*a*b* color space. This method pays more attention to color, edge orientation and perceptually uniform color differences, and encodes color, orientation and perceptually uniform color difference via feature representation in a similar manner to the human visual system. The method can be considered as a novel visual attribute descriptor combining edge orientation, color and perceptually uniform color difference, as well as taking the spatial layout into account without any image segmentation, learning processes or clustering implementation. Experimental results demonstrate that it is much more efficient than the existing image feature descriptors that were originally developed for content-based image retrieval, such as MPEG-7 edge histogram descriptors, color autocorrelograms and multi-texton histograms. It has a strong discriminative power using the color, texture and shape features while accounting for spatial layout.

► We propose a novel histogram for image feature representation. ► It encodes color, orientation and perceptually uniform color difference as feature representation. ► It has good discrimination power of low-level features and spatial layout. ► It needs not any image segmentation and learning processes or clustering implementation.