Exploit Spatial Relationships among Pixels for Saliency Region Detection Using Topic Model.

In this paper, we describe an approach to saliency detection as a two-category (salient or not) soft clustering using topic model. In order to simulate human's paralleled visual neural perception, many subregions are sampling from an image, where each one is considered as a set of colors from a codebook, which is a color palette for the image. We assume salient pixels would appear spatial adjacent more possibly, therefore in a same sub-region, while less salient pixels would either. Consequently, all the sub-regions are clustered into two assumed topics with probabilities: "salient"/ "non-salient", while "salient" one is decided to give saliency value of each pixel according to its posterior conditional probability. Our method will give a global saliency map with full resolution, and experiments illustrate it is competitive with the state-of-art methods. © Springer-Verlag 2013.