Explore double-opponency and skin color for saliency detection

Recent advances in salient detection have exploited the foreground or background information to assist other saliency cues such as contrast to achieve state-of-the-art results. However the problem remains challenging. For example, human skin color is easily overlooked during saliency detection. Simulating the human visual mechanism to improve the current algorithm, we propose a saliency model based on the color-opponent mechanisms of a certain type of color-sensitive double-opponent (DO) cells in the primary visual cortex (V1) of human visual system. Firstly, DO cells with concentric receptive fields (RFs) can detect region contrast for yielding foreground saliency map. Our approach is intuitive and easy to interpret, and it allows fast implementation. Then, skin saliency map is built in a way that is different from high-level factors such as face detection, combining skin region and spatial Euclidean distance weight in the RGB space, and the significant skin features can be obtained effectively. Finally, a linear fusion strategy is proposed to integrate different saliency maps. Experimental results with three well-known benchmark databases demonstrate that the proposed method can achieve competitive performance when compared to state-of-the-art methods. Saliency regions contain important skin features compared to other traditional methods.