Learning a Saliency Map for Fixation Prediction

Linfeng XU  Liaoyuan ZENG  Zhengning WANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.10   pp.2294-2297
Publication Date: 2013/10/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.2294
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.