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Learning a Saliency Map for Fixation Prediction
Linfeng XU Liaoyuan ZENG Zhengning WANG
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2013/10/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
saliency detection, human fixation prediction, bottom-up, top-down,
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In this letter, we use the saliency maps obtained by several bottom-up methods to learn a model to generate a bottom-up saliency map. In order to consider top-down image semantics, we use the high-level features of objectness and background probability to learn a top-down saliency map. The bottom-up map and top-down map are combined through a two-layer structure. Quantitative experiments demonstrate that the proposed method and features are effective to predict human fixation.