Dynamic visual selective attention model

Authors:
Sang-Woo Ban;Inwon Lee;Minho Lee
Affiliations:
Department of Information and Communication Engineering, Dongguk University, 707 Seokjang-Dong, Geyongju, Gyeongbuk 780 714, Republic of Korea;School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, Taegu 702 701, Republic of Korea;School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, Taegu 702 701, Republic of Korea
Venue:
Neurocomputing
Year:
2008

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Abstract

We propose a new biologically motivated dynamic bottom-up selective attention model, which can generate a saliency map (SM) by considering dynamics of continuous input scenes as well as saliency of the primitive features of a static input scene. The maximum entropy algorithm is used to develop the dynamic selective attention model, whereby the input consists of the static bottom-up SMs for the successive static scenes. The experimental results show that the proposed model can generate more plausible scan paths for a dynamic scene compared with those obtained by the static bottom-up attention model.