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Interactive graph cuts are widely used in object seg-mentation but with some disadvantages: 1) Manual interactions may cause inaccurate or even incorrect seg-mentation results and involve more interactions especially for novices. 2) In some situations, the manual interactions are infeasible. To overcome these disadvantages , we propose a novel approach,(More)
Most broadcast stations rely on TV logos to claim video content ownership or visually distinguish the broadcast from the interrupting commercial block. Detecting and tracking a TV logo is of interest to TV commercial skipping applications and logo-based broadcasting surveillance (abnormal signal is accompanied by logo absence). Pixel-wise difference(More)
Image segmentation is a fundamental process in remote sensing image interpretation. It is the basis of the image understanding, such as the region-based change detection for maps updating, the target recognition, and so on [3, 4]. This problem can be seen as a pattern classification application by employing a statistical framework, in which Bayesian(More)
In this paper, we present a variational Bayes (VB) approach for image segmentation. First, image is mod-eled by a mixture model, and then with the techniques of factor analyzer, the underlying structure of image content is inferred automatically. Different from the traditional EM algorithm that seriously suffers from component number selection, the proposed(More)
The paper presents a novel multi-view learning framework based on variational inference. We formulate the framework as a graph representation in form of graph factorization: the graph comprises of factor graphs, which are used to describe internal states of views. Each view is modeled with a Gaus-sian mixture model. The proposed framework has three main(More)