Graph-Regularized Saliency Detection With Convex-Hull-Based Center Prior

作者：Yang, Chuan^*; Zhang, Lihe; Lu, Huchuan

来源：IEEE Signal Processing Letters, 2013, 20(7): 637-640.

DOI：10.1109/LSP.2013.2260737

• Center prior
• contrast prior
• salient object detection
• smoothness prior

Object level saliency detection is useful for many content-based computer vision tasks. In this letter, we present a novel bottom-up salient object detection approach by exploiting contrast, center and smoothness priors. First, we compute an initial saliency map using contrast and center priors. Unlike most existing center prior based methods, we apply the convex hull of interest points to estimate the center of the salient object rather than directly use the image center. This strategy makes the saliency result more robust to the location of objects. Second, we refine the initial saliency map through minimizing a continuous pairwise saliency energy function with graph regularization which encourages adjacent pixels or segments to take the similar saliency value (i.e., smoothness prior). The smoothness prior enables the proposed method to uniformly highlight the salient object and simultaneously suppress the background effectively. Extensive experiments on a large dataset demonstrate that the proposed method performs favorably against the state-of-the-art methods in terms of accuracy and efficiency.

• 出版日期2013-7
• 单位大连理工大学