Multiple Object Saliency Detection Based on Graph and Sparse Principal Component Analysis

In order to detect multiple salient objects from the image with cluttered background, a new multi-object salient detection method based on fully connected graph and sparse principal component analysis is proposed. Firstly, a rapid coarse detection method with different scales is adopted to obtain the object prior with the location of candidate objects and the pixel level saliency map. Meanwhile, we construct a fully connected graph based on the superpixel segmentation to obtain the superpixel-level saliency map. The salient regions are extracted from the superpixel-level binarized salient object prior map and a sparse principal component analysis method is used to gain the main features vector from the pixel matrix composed of the pixels in the optimized salient regions and obtain the salient map of corresponding scale. Finally, the final salient map is fused with the multi-scale saliency maps. Our method takes the advantage of pixel and superpixel method, it can not only simplify the calculation but also improve the detection precision of the salient objects in the image. Quantitative experiments on two public datasets SED2 and HKU_IS demonstrate that out method can detect multiple salient objects from complex images and outperforms other state-of-the-art methods.