Structured-Cut: A Max-Margin Feature Selection Framework for Video Segmentation
نویسنده
چکیده
Segmenting a user-specified foreground object in video sequences has received considerable attention over the past decade. State-ofthe-art methods propose the use of multiple cues other than color in order to discriminate foreground from background. These multiple features are combined within a graph-cut optimization framework and segmentation is predominantly performed on a frame by frame basis. An important problem that arises is the relative weighting of each cue before optimizing the energy function. In this paper, I address the problem of determining the weights of each feature for a given video sequence. More specifically, the implicitly validated segmentation at each frame is used to learn the feature weights that reproduce that segmentation using structured learning. These weights are propagated to the subsequent frame and used to obtain its segmentation. This process is iterated over the entire video sequence. The effectiveness of Structured-Cut is qualitatively demonstrated on sample images and video sequences.
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