期刊
JOURNAL OF VISION
卷 12, 期 6, 页码 -出版社
ASSOC RESEARCH VISION OPHTHALMOLOGY INC
DOI: 10.1167/12.6.22
关键词
AdaBoost; computational saliency model; feature integration
资金
- NeoVision program at DARPA
- ONR
- G. Harold & Leila Y. Mathers Charitable Foundation
- WCU (World Class University) program
- Ministry of Education, Science and Technology through the National Research Foundation of Korea [R31-10008]
To predict where subjects look under natural viewing conditions, biologically inspired saliency models decompose visual input into a set of feature maps across spatial scales. The output of these feature maps are summed to yield the final saliency map. We studied the integration of bottom-up feature maps across multiple spatial scales by using eye movement data from four recent eye tracking datasets. We use AdaBoost as the central computational module that takes into account feature selection, thresholding, weight assignment, and integration in a principled and nonlinear learning framework. By combining the output of feature maps via a series of nonlinear classifiers, the new model consistently predicts eye movements better than any of its competitors.
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