Salient Region Detection Using Diffusion Process on a Two-Layer Sparse Graph.

Diffusion-based salient region detection has recently received intense research attention. In this paper, we present some effective improvements concerning two important aspects of diffusion-based methods: the construction of the diffusion matrix and the seed vector. First, we construct a two-layer sparse graph, which is generated by connecting each node to its neighboring nodes and the most similar node that shares common boundaries with its neighboring nodes. Compared with the most frequently used two-layer neighborhood graph, our graph not only effectively uses local spatial relationships, but also removes dissimilar redundant nodes. Second, we use the spatial variance of superpixel clusters to obtain the seed vector and, compared with the previously most-used boundary prior, our approach can better distinguish saliency seeds from the background seeds, especially when salient objects appear near the image boundaries. Finally, we calculate two preliminary saliency maps using the saliency and background seed vectors, and more accurate results are obtained using the manifold ranking diffusion method. Integrating these two diffusion-based saliency maps, we obtain the final saliency map. Extensive experiments in which we compare our method with 20 existing state-of-the-art methods on five benchmark data sets: ASD, DUT-OMRON, ECSSD, MSRA5K, and MSRA10K, show that the proposed method performs better in terms of various evaluation metrics.