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Learning full-range affinity for diffusion-based saliency detection

Keren Fu (Institutionen för signaler och system, Signalbehandling) ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling) ; Jie Yang
41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016; Shanghai International Convention CenterShanghai; China; 20 March 2016 through 25 March 2016 (1520-6149). p. 1926-1930. (2016)
[Konferensbidrag, refereegranskat]

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.

Nyckelord: affinity learning; graph-based diffusion; Saliency detection; semi-supervised learning



Denna post skapades 2016-07-08.
CPL Pubid: 239215

 

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Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling

Ämnesområden

Signalbehandling

Chalmers infrastruktur