Saliency Detection Using Superpixel Belief Propagation, ICIP2014.
S.-C. Pei, W.-W. Chang and C.-T. Shen, “Saliency Detection using Superpixel Belief Propagation," in Proc. of 21th IEEE International Conference on Image Processing (ICIP), pp. 1135-1139, 2014.
Abstrast: We propose a method to detect saliency from a single image using feature extraction and superpixel belief propagation. We observe that the previous works are hard to deal with the intrinsic material discontinuity and non-homogeneous color distribution within an object or a region. Motivated by this observation, we bring the belief propagation into the saliency detection. First, we separate the image into middlelevel superpixels and also extract the low-level feature within each superpixel. Then, we build up a Markov-Random-Field (MRF) on the middle-level super-pixels and adopt propagation technique to optimize the superpixel saliency. Afterward, we refine this middle-level solution to per-pixel saliency map. Experimental results demonstrate that our proposed method is promising as compared to the state-of-the-art methods in both MSRA-1000 and SED datasets.
Experimental Results on MSRA-1000 Dataset( Input, Superpixels, PR2014, ICCV2013, Ours)
Experimental Results on SED Dataset( Input, Superpixels, CA, RC, Ours)