Visual attention computational model using gabor decomposition and 2d entropy

Authors:
Qi Lv;Bin Wang;Liming Zhang
Affiliations:
Department of Electronic Engineering, Fudan University, Shanghai, China;Department of Electronic Engineering, Fudan University, Shanghai, China;Department of Electronic Engineering, Fudan University, Shanghai, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

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Abstract

Visual attention is an important mechanism as it can be applied to many branches of computer vision and image processing such as segmentation, compression, detection, tracking and so on. Based on both capabilities and defects of existing models, the paper proposes a computational saliency-oriented model from the perspective of frequency domain. A saliency map can be generated by two main steps: firstly Gabor wavelet decomposition of the input image at certain levels is used to produce the feature components, and then these components are selected and fused in the sense of 2D entropy. The proposed algorithm outperforms most of state-of-the-art algorithms at human fixation prediction for both psychological patterns and natural images including salient objects with arbitrary sizes. Beyond that, biological plausibility of Gabor filter makes our approach more reliable and adaptive to various stimuli.