Corner-surround Contrast for saliency detection

Center-surround measurements are widely used for saliency detection but with some disadvantages: 1) Center-surround operation may cause inaccurate segmentation and even involve incorrect detection results; 2) In most situations, only using center-surround feature is not efficient to encode object saliency. To overcome these disadvantages, we describe a novel measurement, namely Corner-Surround Contrast (CSC), to segment salient regions from backgrounds. To explore the effects of CSC feature, a kernel-based fusing framework is designed to produce the saliency map automatically and infer the binary segmentation using graph cut algorithm. The experiments demonstrate the promising performance of our method in terms of segmentation accuracy and saliency localization.