Saliency detection by hierarchically integrating compactness, contrast and boundary connectivity

Saliency detection is one of the most challenging problems in computer vision and has extensive applications in many fields. In this work, instead of simply defining the compactness and contrast, we design novel versions of these two cues based on manifold ranking, and then propose a saliency detection model by integrating the newly modified compactness and contrast with boundary connectivity. Since various scales salient detections highlight different parts of the objects, to further improve the performance, we perform the model hierarchically on four different scales and then fuse the results to obtain the final saliency map. Experiments on four benchmark datasets demonstrate the effectiveness of the proposed method. The method can further improve the accuracy of saliency detection than other 15 state-of-the-art methods on MSRA10k, ASD, DUT-OMRON and ECSSD.