Classification Model of Visual Attention Based on Eye Movement Data
Wang Feng-jia · 2016
Visual attention is a very important part of the human visual system.Most of the existing visual attention models emphasize bottom-up attention,considering less top-down semantic.There is few specific attention model for different categories of images.Eye tracking technology can capture the focus of attention objectively and accurately,but its application in visual attention model is still relatively rare.Therefore,we proposed a classification model of visual attention(CMVA)combining bottom-up with top-down factors,which trains classification models for different categories of images on the basis of eye movement data so as to predict visual saliency.Our model was compared with other existing eight models,proving its superior performance than other models.