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Adaptive Multi-Level Region Merging for Salient Object Detection

Keren Fu (Institutionen för signaler och system, Signalbehandling) ; Chen Gong ; Yixiao Yun (Institutionen för signaler och system, Signalbehandling) ; Yijun Li ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling) ; Jie Yang ; Jingyi Yu
British Machine Vision Conference (BMVC) 2014 p. 11 . (2014)
[Konferensbidrag, refereegranskat]

Most existing salient object detection algorithms face the problem of either under or over-segmenting an image. More recent methods address the problem via multi-level segmentation. However, the number of segmentation levels is manually predetermined and only works well on specific class of images. In this paper, a new salient object detection scheme is presented based on adaptive multi-level region merging. A graph based merging scheme is developed to reassemble regions based on their shared contour strength. This merging process is adaptive to complete contours of salient objects that can then be used for global perceptual analysis, e.g., foreground/ground separation. Such contour completion is enhanced by graph-based spectral decomposition. We show that even though simple region saliency measurements are adopted for each region, encouraging performance can be obtained after across-level integration. Experiments by comparing with 13 existing methods on three benchmark datasets including MSRA-1000, SOD and SED show the proposed method results in uniform object enhancement and achieves state-of-the-art performance.

Nyckelord: Salient object detection, multi-level segmentation, saliency measures



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Denna post skapades 2014-09-09.
CPL Pubid: 202518