Sparse Representation of the Human Vision Information and the Saliency Detection Algorithm
2014 ◽
Vol 513-517
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pp. 3349-3353
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Representation and measurement are two important issues for saliency models. Different with previous works that learnt sparse features from large scale natural statistics, we propose to learn features from short-term statistics of single images. For saliency measurement, we defined basic firing rate (BFR) for each sparse feature, and then we propose to use feature activity rate (FAR) to measure the bottom-up visual saliency. The proposed FAR measure is biological plausible and easy to compute and with satisfied performance. Experiments on human trajectory positioning and psychological patterns demonstrate the effectiveness and robustness of our proposed method.
2014 ◽
Vol 35
(7)
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pp. 1636-1643
Keyword(s):
2012 ◽
Vol 239-240
◽
pp. 811-815
Keyword(s):
2013 ◽
Vol 2013
◽
pp. 1-9
Keyword(s):
Keyword(s):