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Proceedings Paper

Saliency detection by conditional generative adversarial network
Author(s): Xiaoxu Cai; Hui Yu
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Paper Abstract

Detecting salient objects in images has been a fundamental problem in computer vision. In recent years, deep learning has shown its impressive performance in dealing with many kinds of vision tasks. In this paper, we propose a new method to detect salient objects by using Conditional Generative Adversarial Network (GAN). This type of network not only learns the mapping from RGB images to salient regions, but also learns a loss function for training the mapping. To the best of our knowledge, this is the first time that Conditional GAN has been used in salient object detection. We evaluate our saliency detection method on 2 large publicly available datasets with pixel accurate annotations. The experimental results have shown the significant and consistent improvements over the state-of-the-art method on a challenging dataset, and the testing speed is much faster.

Paper Details

Date Published: 10 April 2018
PDF: 7 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061541 (10 April 2018); doi: 10.1117/12.2306421
Show Author Affiliations
Xiaoxu Cai, Univ. of Portsmouth (United Kingdom)
Hui Yu, Univ. of Portsmouth (United Kingdom)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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