Adaptive Multi-Level Region Merging for Salient Object Detection
In Proceedings British Machine Vision Conference 2014
AbstractMost existing salient object detection algorithms face the problem of either under- or over-segmenting an image. More recent solutions 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 which 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 the simplest region saliency measurements are adopted for each region, after across-level integration, encouraging performance can be obtained. Experiments on three benchmark datasets show the proposed method results in uniform object enhancement and achieves state-of-the-art performance.
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