Learning full-range affinity for diffusion-based saliency detection
Paper in proceeding, 2016

In this paper we address the issue of enhancing salient object detection through diffusion-based techniques. For reliably diffusing the energy from labeled seeds, we propose a novel graph-based diffusion scheme called affinity learning-based diffusion (ALD), which is based on learning full-range affinity between two arbitrary graph nodes. The method differs from the previous existing work where implicit diffusion was formulated as a ranking problem on a graph. In the proposed method, the affinity learning is achieved in a unified graph-based semi-supervised manner, whose outcome is leveraged for global propagation. By properly selecting an affinity learning model, the proposed ALD outperforms the ranking-based diffusion in terms of accurately detecting salient objects and enhancing the correct salient objects under a range of background scenarios. By utilizing the ALD, we propose an enhanced saliency detector that outperforms 7 recent state-of-the-art saliency models on 3 benchmark datasets.

graph-based diffusion

affinity learning

Saliency detection

semi-supervised learning


Keren Fu

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Irene Yu-Hua Gu

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Jie Yang

Shanghai Jiao Tong University

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

15206149 (ISSN)

Vol. 2016-May 1926-1930
978-1-4799-9988-0 (ISBN)

Subject Categories

Signal Processing





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