Synergetic Multi-Semantic Multi-Instance Learning for Scene Recognition
2012 ◽
Vol 220-223
◽
pp. 2188-2191
Keyword(s):
In this paper, the problem of scene representation is modeled by simultaneously considering the stimulus-driven and instance-related factors in a probabilistic framework. In this framework, a stimulus-driven component simulates the low-level processes in human vision system using semantic constrain; while a instance-related component simulate the high-level processes to bias the competition of the input features. We interpret the synergetic multi-semantic multi-instance learning on five scene database of LabelMe benchmark, and validate scene classification on the fifteen scene database via the SVM inference with comparison to the state-of-arts methods.
Image Quality Assessment Method Based on Contrast Sensitivity Characteristics of Human Vision System
2011 ◽
Vol 26
(3)
◽
pp. 390-396
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2019 ◽
Vol 33
(06)
◽
pp. 1955007
Keyword(s):
Keyword(s):
2012 ◽
Vol 157-158
◽
pp. 410-414
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Keyword(s):
2010 ◽
Vol 3
(3)
◽
pp. 391-409
◽
1987 ◽
Vol 389
(1)
◽
pp. 1-21
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