PROBABILISTIC SALIENT OBJECT CONTOUR DETECTION BASED ON SUPERPIXELS

In this paper, we propose a data-driven approach to detect the probabilistic salient object contour, which is formulated as predicting the probability of superpixel boundaries being on the object contour based on the learned regressor. Each superpixel boundary is jointly described by the superpixel saliency, superpixel contrast, and boundary geometry features. Experimental results on the benchmark data set validate the effectiveness of our approach. Furthermore, we demonstrate that the predicted probabilistic salient object contour is useful for improving the multiple segmentations for salient object detection.