Perceptual color descriptor based on a spatial distribution model: Proximity histograms

Abstract

Color is the main source of information particularly for content-analysis and retrieval. Most of the color descriptors, however, show severe limitations and drawbacks due to their incapability of modeling the human color perception. Moreover, they cannot characterize all the properties of the color composition in visual scenery. In this paper we present a perceptual color feature, which describes all major properties of prominent colors both in spatial and color domain. In accordance with the well-known Gestalt law, we adopt a top-down approach in order to model (see) the whole color composition before its parts and in this way we can avoid the problems of pixel-based approaches. In color domain the dominant colors are extracted along with their global properties and quad-tree decomposition partitions the image so as to characterize the spatial color distribution (SCD). The proposed color model distills the histogram of inter-color distances. Combination of the extracted global and spatial properties forms the final descriptor, which is neither biased nor become noisy from the presence of such color elements that cannot be perceived in both spatial and color domains. Finally a penalty-trio model fuses all color properties in a similarity distance computation during retrieval. Experimental results approve the superiority of the proposed technique against well-known global and spatial descriptors.