Superpixel-Fusion Based Salient Region Detection

According to the problem that some saliency detection algorithm can't reflect saliency information exactly, a new saliency detection method based on superpixel-fusion was proposed. Firstly, superpixel segmentation operation was executed for the input image, then the graph model with superpixels as nodes was built. Secondly, by computing the superpixel adjacency matrix, the graph model was transfered to minimum spanning tree model. Thirdly, the threshold can be fixed by using OTSU algorithm which was the standard to fuse part of nodes of the MST to gain big superpixel region. At last, the saliency maps were computed via the color and space distance between big superpixels. Compared with other detection methods, experimental results showed that this algorithm can detect the salient object more efficient and nearly reach the segmentation effect.