摘要：This paper proposes a bottom-up saliency detection algorithm based on multi-dictionary sparse recovery. Firstly, the SLIC algorithm is used to segment the image into superpixels in multilevel and atoms with a high background possibility are selected from the boundary superpixels to construct the multidictionary. Secondly, sparse recovery of the entire image is achieved using multi-dictionary to get subsaliency maps from the perspective of sparse recovery errors. The final saliency map is generated in a weighted fusion manner. Experimental results on three public datasets demonstrate the effectiveness of our model.
