Semantic meaning modulates object importance in human fixation prediction

Abstract

Humans tend to allocate attention to semantic entities. Objects are important in fixation selection, but not all the objects are equally attractive. In this paper, we introduce the concept of attribute bias to characterize the influence of semantic attributes compared with low-level saliency on fixation distribution. Two different ways are adopted to get two sets of semantic attributes. In both cases, most semantic attributes have a positive influence on drawing attention and contribute more than low-level saliency in object areas. We also find that attribute bias is robust to low-level saliency and can consistently reflect the relative attractiveness of objects with different semantic attributes. It is demonstrated that such bias helps make better fixation predictions by distinguishing the importance of objects, although low-level saliency models with better performance are less dramatically improved by attribute bias. These findings indicate the role of conceptual meaning as opposed to features in visual attention.