Saliency Detection Method Using Hypergraphs on Adaptive Multiscales

Saliency detection plays an important role in the fields of image processing and computer vision. We present an improved saliency detection method by means of hypergraphs on adaptive multi-scales (HAM). It first adjusts adaptively the ranges of pixel-values in ${R}, {G}, {B}$ channels in an input image and uses the three ranges to determine adaptive scales for the construction of hypergraphs. And then, the image is modeled as multiple hypergraphs on adaptive scales in which hyper-edges are clustered by means of the agglomerative mean-shift method. The HAM method can get more single-scale hypergraphs and thus has higher accuracy than the previous ones because hypergraphs are constructed on adaptive multi-scales instead of fixed scales. Extensive experiments on the published benchmark datasets demonstrate that the HAM method has improved the performance of detecting salient objects in input images, especially for the images with narrow ranges of pixel-values.