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Computer Vision and Image Understanding
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Signal Processing - Video segmentation for content-based processing manipulation
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
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International Journal on Digital Libraries
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Multimedia Tools and Applications
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Pattern Recognition Letters
Key frame extraction based on visual attention model
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ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
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IEEE Transactions on Multimedia
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IEEE Transactions on Consumer Electronics
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IEEE Transactions on Consumer Electronics
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IEEE Transactions on Image Processing
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IEEE Transactions on Image Processing
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IEEE Transactions on Image Processing
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Unsupervised extraction of visual attention objects in color images
IEEE Transactions on Circuits and Systems for Video Technology
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This paper addresses a novel approach to automatically extract video salient objects based on visual attention mechanism and seeded object growing technique. First, a dynamic visual attention model to capture the object motions by global motion estimation and compensation is constructed. Through combining it with a static attention model, a saliency map is formed. Then, with a modified inhibition of return (MIOR) strategy, the winner-take-all (WTA) neural network is used to scan the saliency map for the most salient locations selected as attention seeds. Lastly, the particle swarm optimization (PSO) algorithm is employed to grow the attention objects modeled by Markov random field (MRF) from the seeds. Experiments verify that our presented approach could extract both of stationary and moving salient objects efficiently.