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In this paper, we propose an unsupervised objective measure for quality evaluation of single object segmentation in images. Objectness as an essential attribute of objects is treated as a main feature to measure object segmentation quality. In addition, the prior information about the object quantity is integrated into the proposed measure. Experimental results show that our measure can conform well...
In this paper, we propose a novel switch scheme and a saliency map binarization method for salient object segmentation. With the proposed switch scheme, the saliency map can be segmented by different methods according to its quality, which is evaluated by a method proposed in this paper. We also develop a binarization method by integrating three properties of the salient object. This method exclusively...
In this paper, we propose an unsupervised salient object segmentation approach based on kernel density estimation (KDE) and two-phase graph cut. A set of KDE models are first constructed based on the pre-segmentation result of the input image, and then for each pixel, a set of likelihoods to fit all KDE models are calculated accordingly. The color saliency and spatial saliency of each KDE model are...
Interactive object segmentation is widely used for extracting any user-interested objects from natural images. A common problem with many interactive segmentation approaches is that the object segmentation quality is degraded due to inaccurate object/background seeds provided by the user. This paper proposes an iterative adjustable graph cut to efficiently solve this problem. First, object/background...
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