A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual attention detection in video sequences using spatiotemporal cues
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
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Salient region detection and segmentation
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Saliency estimation using a non-parametric low-level vision model
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Unsupervised extraction of visual attention objects in color images
IEEE Transactions on Circuits and Systems for Video Technology
Visual saliency detection with center shift
Neurocomputing
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In this paper, a new method for saliency detection is proposed. Based on the defined features of the salient object, we solve the problem of saliency detection from three aspects. Firstly, from the view of global information, we partition the image into two clusters, namely, salient component and background component by employing Principal Component Analysis (PCA) and k-means clustering. Secondly, the maximal salient information is applied to find the position of saliency and eliminate the noise. Thirdly, we enhance the saliency for the salient regions while weaken the background regions. Finally, the saliency map is obtained based on these aspects. Experimental results show that the proposed method achieves better results than the state of the art methods. And this method can be applied for graph based salient object segmentation.