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Proceedings Paper

Probability-based saliency detection approach for multi-features integration
Author(s): Jing Pan; Yuqing He; Qishen Zhang; Kun Huang
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Paper Abstract

There are various saliency detection methods have been proposed recent years. These methods can often complement each other so combining them in appropriate way will be an effective solution of saliency analysis. Existing aggregation methods assigned weights to each entire saliency map, ignoring that features perform differently in certain parts of an image and their gaps between distinguishing the foreground from the backgrounds. In this work, we present a Bayesian probability based framework for multi-feature aggregation. We address saliency detection as a two-class classification problem. Saliency maps generated from each feature have been decomposed into pixels. By the statistic results of different saliency value’s reliability on foreground and background detection, we can generate an accurate, uniform and per-pixel saliency mask without any manual set parameters. This approach can significantly suppress feature’s misclassification while preserve their sensitivity on foreground or background. Experiment on public saliency benchmarks show that our method achieves equal or better results than all state-of-the-art approaches. A new dataset contains 1500 images with human labeled ground truth is also constructed.

Paper Details

Date Published: 13 April 2015
PDF: 8 pages
Proc. SPIE 9522, Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics 2014, Part II, 95222R (13 April 2015); doi: 10.1117/12.2182163
Show Author Affiliations
Jing Pan, Beijing Institute of Technology (China)
Yuqing He, Beijing Institute of Technology (China)
Qishen Zhang, Beijing Institute of Technology (China)
Kun Huang, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 9522:
Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics 2014, Part II
Xiangwan Du; Jennifer Liu; Dianyuan Fan; Jialing Le; Yueguang Lv; Jianquan Yao; Weimin Bao; Lijun Wang, Editor(s)

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