Cluster-Based Co-Saliency Detection
TLDR
This paper introduces a new cluster-based algorithm for co-saliency detection that is mostly bottom-up without heavy learning, and outperforms most the state-of-the-art saliency detection methods.Abstract:
Co-saliency is used to discover the common saliency on the multiple images, which is a relatively underexplored area. In this paper, we introduce a new cluster-based algorithm for co-saliency detection. Global correspondence between the multiple images is implicitly learned during the clustering process. Three visual attention cues: contrast, spatial, and corresponding, are devised to effectively measure the cluster saliency. The final co-saliency maps are generated by fusing the single image saliency and multiimage saliency. The advantage of our method is mostly bottom-up without heavy learning, and has the property of being simple, general, efficient, and effective. Quantitative and qualitative experiments result in a variety of benchmark datasets demonstrating the advantages of the proposed method over the competing co-saliency methods. Our method on single image also outperforms most the state-of-the-art saliency detection methods. Furthermore, we apply the co-saliency method on four vision applications: co-segmentation, robust image distance, weakly supervised learning, and video foreground detection, which demonstrate the potential usages of the co-saliency map.read more
Citations
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Salient Object Detection: A Survey
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Saliency-aware geodesic video object segmentation
TL;DR: This work introduces an unsupervised, geodesic distance based, salient video object segmentation method that incorporates saliency as prior for object via the computation of robust geodesIC measurement and builds global appearance models for foreground and background.
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Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework
TL;DR: A new SP-MIL framework for co-saliency detection is proposed, which integrates both multiple instance learning (MIL) and self-paced learning (SPL) into a unified learning framework.
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Proceedings ArticleDOI
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