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Saliency detection from one time sampling for eye fixation prediction

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Abstract

Saliency modeling has become one of the most popular studies in computer vision. Many previous works adopted distinctness to compute saliency score of an image element, which usually need point-to-point distances calculation and it is quadratic complexity. In this paper, a visual saliency model based on one time sampling outlier detection is proposed, and the time complexity is linear to image size, further analyses and experiments demonstrate that our model is robust and efficient. This model is parameter insensitive, without learning, and easy to implement. Extensive experiments on four benchmark datasets show that our model is competitive compare with state-of-the-art models under shuffled AUC metric.

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Notes

  1. The term ”data set” in this paper is a data cluster which consists of inliers and outliers, differ from eye-tracking dataset for saliency detection.

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Acknowledgments

He Tang would like to thank Yanan Bie who proof read this article at various stages.

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Authors and Affiliations

  1. School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China

    He Tang, Chuanbo Chen & Xiaobing Pei

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  1. He Tang
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Correspondence to He Tang.

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Tang, H., Chen, C. & Pei, X. Saliency detection from one time sampling for eye fixation prediction. Multimed Tools Appl 77, 165–184 (2018). https://doi.org/10.1007/s11042-016-4248-7

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