Extraction of salient objects based on image clustering and saliency
Over the past decades, numerous methods have been proposed on salient object detection. However, most of these methods need users interactions as a prerequisite to control their progress. In this paper, we propose a novel method for extraction of salient objects based on image clustering and saliency map from natural scene images. This method is a combination of image clustering, saliency map generation and automatic initialization. First, a graph based clustering method is applied to split the input image into regions. Second, a saliency map of the input image is generated using the contrast among split regions. From the split regions and generated saliency map, an adaptive threshold is defined, which classify the split regions into foreground and background. After that, the initial mask for object detection is determined using the classified foreground and background clusters and saliency values. A grab-cut with our initial mask is applied to extract the objects of interest, and the experimental results have shown that our proposed method is able to replace manual labeling of initialization in object detection.
saliency map automatic initialization graph based clustering grab-cut