关键词: 显著性检测, 马尔科夫链, 随机游走模型, 超像素, Wasserstein距离

Abstract: Current visual saliency detection algorithms mainly base on contrast of pixels, and most of the algorithms ignore the boundaries by selecting pixels in a rigid way. According to biological visual attention, this paper presents a new method for saliency detection based on Markov chain and superpixels. The paper extracts superpixels from the original image, and defines a Markov chain with the Wasserstein distance between superpixels, thus the problem of saliency detection is formulated as a Markov random walk model. Equilibrium distribution of the Markov chain is the result of saliency detection. Experimental results show that this saliency detection algorithm achieves a satisfactory result in extraction accuracy of main target and its boundaries compared with two representative algorithms.

Key words: saliency detection, Markov chain, random walk model, superpixel, Wasserstein distance