SSG: superpixel segmentation and GrabCut-based salient object segmentation

摘要

Saliency detection is a popular topic for image processing recently. In this paper, we propose a simple, robust and fast salient object segmentation framework. Firstly, we develop a novel saliency map segmentation strategy, named SSG which consists of superpixel region growing, superpixel Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering and iterated graph cuts (GrabCut), where DBSCAN makes similar background regions cluster as a whole, region growing groups similar regions together as much as possible, GrabCut segments salient objects accurately. Then, the proposed SSG is combined with saliency detection to abstract salient objects. Experimental results on three benchmark datasets demonstrate that the proposed method achieves the favorable performance than many recent state-of-the-art methods in terms of precision, recall, F-measure and execution time.