Top-Down Object Color Biased Attention Using Growing Fuzzy Topology ART

Authors:
Byungku Hwang;Sang-Woo Ban;Minho Lee
Affiliations:
School of Electrical Engineering and Computer Science, Kyungpook National University, Puk-Gu, Korea 702-701;Dept. of Information and Communication Engineering, Dongguk University, Gyeongju, Korea 780-714;School of Electrical Engineering and Computer Science, Kyungpook National University, Puk-Gu, Korea 702-701
Venue:
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Year:
2008

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Abstract

In this paper, we propose a top-down object biased attention model which is based on human visual attention mechanism integrating feature based bottom-up attention and goal based top-down attention. The proposed model can guide attention to focus on a given target colored object over other objects or feature based salient areas by considering the object color biased attention mechanism. We proposed a growing fuzzy topology ART that plays important roles for object color biased attention, one of which is to incrementally learn and memorize features of arbitrary objects and the other one is to generate top-down bias signal by competing memorized features of a given target object with features of an arbitrary 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.