Salient Object Detection based on Boundary Contrast with Regularized Manifold Ranking
Luo, Yongkang1; Wang, Peng1; Li, Wanyi1; Shang, Xiaopeng2; Qiao, Hong3,4; Wang Peng
2016-06-12
会议名称12th World Congress on Intelligent Control and Automation (WCICA)
会议录名称12th World Congress on Intelligent Control and Automation (WCICA)
会议日期June 12-15, 2016
会议地点Guilin, China
摘要Salient object detection via graph-based manifold ranking, which exploits the boundary prior by using image boundaries as labelled background queries, always achieves impressive performance. However, when the salient object broadly touches the image boundary, this method is fragile and may fail. To address this issue, we present a novel approach which bases on boundary contrast with regularized manifold ranking. First, we compute the contrast saliency against the image boundary as ranking queries, instead of directly using the boundaries as background queries. Second, we use an affinity matrix with regularization for manifold ranking to infer saliency value. Third, we integrate saliency inference result with foregroundness based on boundary connectivity to improve the detection accuracy. Last, we adopt multiscale method to mitigate the object scale effect in saliency detection. Experimental results on three benchmark datasets show that the proposed method achieves comparable or better performance than stat-of-the-art methods.
关键词Salient Object Detection Manifold Ranking Contrast Saliency Boundary Connectivity
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11606
专题精密感知与控制研究中心_精密感知与控制
通讯作者Wang Peng
作者单位1.The Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences
2.Beijing Aerospace Automatic Control Institute
3.The State Key Lab of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
4.CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT)
推荐引用方式
GB/T 7714
Luo, Yongkang,Wang, Peng,Li, Wanyi,et al. Salient Object Detection based on Boundary Contrast with Regularized Manifold Ranking[C],2016.
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