Top-down visual selective attention model combined with bottom-up saliency map for incremental object perception

Abstract

Humans can efficiently perceive arbitrary visual objects based on incremental learning mechanism and selective attention function. In this paper, we propose a new top-down attention model based on human visual attention mechanism, which considers both relative feature based bottom-up saliency and goal oriented top-down attention. The proposed model can generate top-down bias signals of form and color features for a specific object, which draw attention to find a desired object by an incremental learning mechanism together with object feature representation scheme. A growing fuzzy topology adaptive resonance theory (GFTART) model is proposed by adapting a growing cell structure (GCS) unit into a conventional fuzzy ART, by which the proliferation problem of the conventional fuzzy ART can be enhanced. The proposed GFTART plays two important roles for object color and form biased attention; one is to incrementally learn and memorize color and form features of arbitrary objects, and the other is to generate top-down bias signal for selectively attending to a target object. Experimental results show that the proposed model performs well in successfully focusing on given target objects, as well as incrementally perceiving arbitrary objects in natural scenes.