Co-saliency detection via seed propagation over the integrated graph with a cluster layer
Insung Hwang, Dong-ju Jeong, Jae Sung Park, Nam Ik Cho · 2017
This paper presents a method to detect common salient regions in a set of images. Since saliency and co-saliency detection are usually used as a pre-processing step for image processing and vision tasks, it is important to consider the complexity as well as the performance of an algorithm. Thus, we adhere to using low-level features and propose to detect co-salient objects with a new multilayer graph model in a bottom-up manner. Input images are represented by intra- and inter-image graphs composed of superpixels and their clusters, and each of these nodes obtains its initial co-saliency value from several cues. To generate resultant co-saliency values, foreground and background seeds are defined at parts of the unified multilayer graph, over which the seeds are propagated. Our experiments show that the proposed algorithm outperforms comparable methods on widely used public datasets, especially for the images that have various features.