Computational Model of Intention-Oriented Visual Attention

Abstract

Human attention behavior relies not only on the natural contrastive information but also much top-down information in the mind. The intention represents preference or repugnance for specific image property undoubtedly affects the visual attention. By introducing the intentional features and the fuzzy inference system into the computational model, the Intention-oriented Computational Visual Attention (ICVA) is developed and shown able to combine the intention with the optical excitation to And the attentive spots. An intention about favorite colors, a desired object or even multiple desired objects is expressed as a combination of preference vectors and topological relationships. Referring to the preference vectors, the intentional features are extracted. According to the topological relationships, the linguistic rules of the fuzzy inference system are built to infer the results. Several experiments have been conducted and the results verify the feasibility of the proposed design.