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Multi-label learning is widely applied in many tasks, where an object possesses multiple concepts with each represented by a class label. Previous studies on multi-label learning have focused on a fixed set of class labels, i.e., the class label set of test data is the same as that in the training set. In many applications, however, the environment is open and new concepts may emerge with previously...
Tracking-by-detection based on online learning has shown superior performance in visual tracking of unknown objects. However, most existing approaches use a fixed-size box to represent objects and can merely show the unoccluded area of the object. To overcome the limitations, we propose a novel tracking-by-detection approach based on local patches. We extend ferns forest to visual tracking and optimize...
To learn an object detector labeled training data is required. Since unlabeled training data is often given as an image sequence we propose a tracking-based approach to minimize the manual effort when learning an object detector. The main idea is to apply a tracker within an active on-line learning framework for selecting and labeling unlabeled samples. For that purpose the current classifier is evaluated...
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