An analysis of multi-level color histograms

Color is one of the most recognizable elements of image content, and color histogram is the most commonly used technique for indexing colors. Faloutsos et al. propose using a 3-dimensional index to perform histogram filtering.(3) Sawhney and Hafner later generalize the filtering approach by using L-dimensional indices.(11) The main contribution of this paper is the development and analysis of multi-level color histograms. The key idea is to insert additional levels of abstracted histograms in between a low dimensional index and the original histograms. Based on a cost model we developed, our analysis shows that in most cases, the optimal 3-level and 4-level configurations, when compared with the Faloutsos configuration and the optimal Sawhney-Hafner configuration, require lower CPU and I/O costs. Experimental results indicate that the gain in total time can vary from 22% to 400%. Our analysis also shows that the overhead required by 3-level and 4-level histograms is negligible.