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An efficient segmentation method using saliency object detection
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DOI：10.1007/s11042-014-1871-z.png)
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Abstract
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Automatic co-segmentation is a challenging task because it lacks of prior cues. In this paper, an efficient region contrast based method is proposed for salient object detection and segmentation. The coarse location information of the salient object and the background is first estimated based on the distribution of the detected key-points. Histograms of the estimated foreground and background are calculated as their features. An image is then over-segmented into super-pixels and their histograms are computed. The saliency of a super-pixel is obtained according to the similarity coefficients between the super-pixel and the estimated foreground/background. With the saliency map, the salient object in the image is extracted using a graph cut based optimized framework. The proposed method is compared with state-of-the-art methods on the widely used dataset, and the experiments show that it overall obtains more accurate results.
Keywords:
Object segmentation
Saliency detection
Similarity measurement
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