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This paper proposes a nonparametric saliency model based on kernel density estimation (KDE) mainly aiming at content-based applications such as salient object segmentation. A set of KDE models are constructed on the basis of regions segmented using the mean shift algorithm. For each pixel, a set of color likelihood measures to all KDE models are calculated, and then the color saliency and spatial...
This paper proposes an efficient and flexible interactive object segmentation approach using kernel density estimation based graph cuts. First, the user draws scribbles to roughly mark the interested object and background, respectively, and the likelihood of object versus background is evaluated for each pixel using nonparametric kernel density estimation. Then pixels are globally classified into...
In this paper, we propose a novel video object tracking approach based on kernel density estimation and Markov random field (MRF). The interested video objects are first segmented by the user, and a nonparametric model based on kernel density estimation is initialized for each video object and the remaining background, respectively. A temporal saliency map is also initialized for each object to memorize...
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