Zhao Qi, Koch Christof
Computation and Neural Systems, California Institute of Technology, Pasadena, CA, USA.
J Vis. 2012 Jun 15;12(6):22. doi: 10.1167/12.6.22.
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.
为了预测在自然观看条件下受试者的注视位置，受生物启发的显著性模型将视觉输入分解为跨空间尺度的一组特征图。这些特征图的输出被求和以产生最终的显著性图。我们通过使用来自四个最近的眼动追踪数据集的眼动数据，研究了跨多个空间尺度的自下而上特征图的整合。我们使用AdaBoost作为核心计算模块，该模块在一个有原则的非线性学习框架中考虑特征选择、阈值处理、权重分配和整合。通过一系列非线性分类器组合特征图的输出，新模型始终比任何竞争对手更好地预测眼动。
