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This paper proposes a video object segmentation algorithm based on the conditional random field (CRF) framework. A foreground object in the first frame is segmented by training the CRF on user interaction, i.e., by using user scribbles corresponding to foreground and background respectively for CRF training. The data term of the energy function in this CRF framework is designed as a function of the...
This paper proposes a new approach to the feature based unsupervised image segmentation. The difficulty with the conventional unsupervised segmentation lies in finding appropriate features that discriminate a meaningful region from the others. In this paper, the appropriate features are automatically learnt by machine learning with boosting scheme. At the initial step, the image is split into many...
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