Perceptual color descriptor based on spatial distribution: A top-down approach

Color features are the key-elements widely used in content-analysis and retrieval. However, most of them show severe limitations and drawbacks due to their inefficiency of modeling the human visual system with respect to color perception. Moreover, they cannot characterize all the properties of the color composition in a visual scenery. In this paper we present a perceptual color feature, which describes all major properties of prominent colors both in spatial and color domains. In accordance with the well-known Gestalt law, we adopt a global, 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). We propose two efficient SCD descriptors; the proximity histograms, which distill the histogram of inter-color distances and the proximity grids, which cumulate the spatial co-occurrence of colors in a 2D grid. Both approaches are configurable and provide means of modeling SCD in a scalar and directional way. Combination of the extracted global and spatial properties forms the final descriptor, which is unbiased and robust to non-perceivable color elements 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 powerful global and spatial color descriptors. Perceptual Color Descriptor based on Spatial Distribution: A Top-Down Approach 1 Abstract—Color features are the key-elements widely used in content-analysis and retrieval. However, most of them show severe limitations and drawbacks due to their inefficiency of modeling the human visual system with respect to color perception. Moreover, they cannot characterize all the properties of the color composition in a visual scenery. In this paper we present a perceptual color feature, which describes all major properties of prominent colors both in spatial and color domains. In accordance with the well-known Gestalt law, we adopt a global, 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). We propose two efficient SCD descriptors; the proximity histograms, which distill the histogram of inter-color distances and the proximity grids, which cumulate the spatial co-occurrence of colors in a 2D grid. Both approaches are configurable and provide means of modeling SCD in a scalar and directional way. Combination of the extracted global and spatial properties forms the final descriptor, which is unbiased and robust to non-perceivable color elements 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 powerful global and spatial color descriptors.Color features are the key-elements widely used in content-analysis and retrieval. However, most of them show severe limitations and drawbacks due to their inefficiency of modeling the human visual system with respect to color perception. Moreover, they cannot characterize all the properties of the color composition in a visual scenery. In this paper we present a perceptual color feature, which describes all major properties of prominent colors both in spatial and color domains. In accordance with the well-known Gestalt law, we adopt a global, 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). We propose two efficient SCD descriptors; the proximity histograms, which distill the histogram of inter-color distances and the proximity grids, which cumulate the spatial co-occurrence of colors in a 2D grid. Both approaches are configurable and provide means of modeling SCD in a scalar and directional way. Combination of the extracted global and spatial properties forms the final descriptor, which is unbiased and robust to non-perceivable color elements 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 powerful global and spatial color descriptors.