Bottom-Up Saliency Prediction by Simulating End-Stopping with Log-Gabor

This paper presents a bottom-up saliency model inspired by end-stopping mechanism in primary visual cortex (V1). By modelling an end-stopped cell as multiplication of the outputs from two different orientations tuned selective neurons, corners, line intersections, and line endings, which are called end-stopping features in this paper, are extracted and integrated to indicate saliency cues. The proposed model is constructed as follow: firstly we utilize log-Gabor filters to represent orientation selectivity in V1 neurons; then energy maps of the log-Gabor response from two different orientations are multiplied to extract median features perceived by end-stopped cells; finally the resulting feature maps are combined with color features computed by the traditional center-surround operation to obtain the final saliency map. Results on public eye tracking datasets show the proposed model achieves state-of-the-art performance compared to other models.