Saliency detection via two-directional 2DPCA analysis of image patches

Salient object detection is an important and challenging problem in computer vision. In this paper, we present a model of salient region detection based on the fusion of contrast and distribution, computed by two-directional 2DPCA analysis of image patches under the combination of RGB space, LAB space and YCbCr space. First, non-overlap patches of three layers from the image are obtained in the three color spaces respectively and stacked for the combination of the three sapces in a single layer. For every layer, two-directional, two-dimensional PCA are utilized to realize automatic selection of effective features, then based on the high contrast and compact character of salient object, contrast values and distribution values of image patches are fused to get the saliency map. Finally, three saliency maps for three layers are combined to detect salient object. The experimental results on a publicly available database show that the proposed algorithm performs well and are in line with the human eye observation results.