Directional Spatial Color Descriptor in a Perceptual Model: Proximity Grids

Most of the color features widely used in content-based image retrieval (CBIR) present severe limitations and drawbacks due to their inefficiency of modeling human visual system on color perception. Accordingly, they are not capable of characterizing both spatial and global properties of the color composition in visual scenery. In this paper, we present a perceptual color feature, which describes the global properties of the prominent colors along with a directional spatial descriptor, called as Proximity Grids. 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). This approach is in accordance with the well-known Gestalt law, i.e. utilizing 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. The proximity grids, which cumulate the spatial co-occurrence of colors in a 2D grid, can successfully model the SCD of the prominent colors with respect to inter-color proximities and directions. Fusing both global and spatial properties forms the final descriptor, which is neither biased nor become noisy from the presence of such color elements, the so-called outliers that are not visible for humans in both spatial and color domains. Finally a penalty-trio model cumulates the differences among the color properties in a similarity distance computation during retrieval. Comparative evaluations against well-known global and spatial descriptors demonstrate the superiority of the proposed descriptor.