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Spectral salient object detection

Keren Fu (Institutionen för signaler och system, Signalbehandling) ; C Gong ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling) ; Jie Yang ; Xiangjian He
Proceedings - IEEE International Conference on Multimedia and Expo (1945-7871). Vol. 2014-September (2014), Septmber, p. 6.
[Konferensbidrag, refereegranskat]

Many existing methods for salient object detection are performed by over-segmenting images into non-overlapping regions, which facilitate local/global color statistics for saliency computation. In this paper, we propose a new approach: spectral salient object detection, which is benefited from selected attributes of normalized cut, enabling better retaining of holistic salient objects as comparing to conventionally employed pre-segmentation techniques. The proposed saliency detection method recursively bi-partitions regions that render the lowest cut cost in each iteration, resulting in binary spanning tree structure. Each segmented region is then evaluated under criterion that fit Gestalt laws and statistical prior. Final result is obtained by integrating multiple intermediate saliency maps. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method against 13 state-of-the-art approaches to salient object detection.

Nyckelord: Salient object detection, Normalized cut, Pre-segmentation, Partition, Gestalt laws

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Denna post skapades 2014-05-14. Senast ändrad 2015-10-26.
CPL Pubid: 198048


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