User-Specific Visual Attention Estimation Based on Visual Similarity and Spatial Information in Images
Yuya Moroto, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama · 2019
This paper presents a method for user-specific visual attention estimation based on visual similarities and spatial information in images. In order to estimate the user- specific visual attention, the proposed method calculates two kinds of saliency maps. One is constructed as a visual similarity-based saliency map, and the other is constructed by considering spatial information of objects in images. The proposed method performs a fusion of these two maps for considering visual similarities and spatial information. This is the biggest contribution of this paper. Therefore, improvement of the estimation performance of the user-specific visual attention is realized.