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论文类型：会议论文

发表时间：2015-09-22

收录刊物：EI、CPCI-S、Scopus

页面范围：25-30

关键字：Saliency; MI-KSVD; smoothing; multi-scale; object location

摘要：In this paper, we propose a visual saliency detection algorithm with MI-KSVD, a codebook learning algorithm that balances reconstruction error and mutual incoherence of the codebook. We first segment the images into superpixels by simple linear iterative clustering (SLIC), which can improve the efficiency and correctness of the progress. Then we calculate the reconstruction errors based on the initial background propagated from the boundaries of the image. We use a weighted sum of multi-scale region-level saliency as the pixel-level saliency in order to generate a more continuous and smooth result. Based on that, we further use object recognition as a vital prior to improve the performance of our method. Experimental results on three benchmark datasets show that the proposed method performed well to reach our expectations in terms of precision and recall.