Saliency Detection with Sparse Prototypes: An Approach Based on Multi-Dictionary Sparse Encoding

Jun Wang, Zemin Wu, Chang Tian, Lei Hu, Changde Lu, Xuesheng Pei, Jianning Su
2018 MATEC Web of Conferences  
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
more » ... ed in a weighted fusion manner. Experimental results on three public datasets demonstrate the effectiveness of our model.
doi:10.1051/matecconf/201817603009 fatcat:nkoc33sekzfrrmkcimd5ipzlwa