Visual saliency can be a useful tool for image content analysis such as automatic image cropping and image compression. In existing methods on visual saliency detection, most of them are related to the model of receptive field. In this paper, we propose a bottom-up model which introduces 2D Log-Gabor wavelets for saliency detection. Compared with the traditional model of receptive field, the 2D Log-Gabor wavelets can better simulate the biological characteristics of the simple cortical cell in the receptive filed. Moreover, we also incorporate the influence of center bias into our model, which is a common phenomenon that directs visual attention to the center of images in natural scenes. Experimental results show that our approach outperforms three state-of-the-art approaches remarkably.
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