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• W1966261683 abstract "This paper proposes to learn a discriminative dictionary for saliency detection. In addition to the conventional sparse coding mechanism that learns a representational dictionary of natural images for saliency prediction, this work uses supervised information from eye tracking experiments in training to enhance the discriminative power of the learned dictionary. Furthermore, we explicitly model saliency at multi-scale by formulating it as a multi-class problem, and a label consistency term is incorporated into the framework to encourage class (salient vs. non-salient) and scale consistency in the learned sparse codes. K-SVD is employed as the central computational module to efficiently obtain the optimal solution. Experiments demonstrate the superior performance of the proposed algorithm compared with the state-of-the-art in saliency prediction." @default.
• W1966261683 created "2016-06-24" @default.
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• W1966261683 date "2013-10-01" @default.
• W1966261683 modified "2023-09-26" @default.
• W1966261683 title "Leveraging Human Fixations in Sparse Coding: Learning a Discriminative Dictionary for Saliency Prediction" @default.
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• W1966261683 doi "https://doi.org/10.1109/smc.2013.364" @default.
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