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Information retrieval in large databases of complex objects, such as images, audio or documents, requires approximate search algorithms in practice, in order to return semantically similar objects to a given query in a reasonabletime. One practical approach is supervised binary hashing, where each object is mapped onto a small binary vector so that Hamming distances approximate semantic similarities,...
An attractive approach for fast search in image databases is binary hashing, where each high-dimensional, real-valued image is mapped onto a low-dimensional, binary vector and the search is done in this binary space. Finding the optimal hash function is difficult because it involves binary constraints, and most approaches approximate the optimization by relaxing the constraints and then binarizing...
Since tracking algorithms should be robust with respect to appearance changes, online algorithms has been investigated recently instead of offline ones which has shown an acceptable performance in controlled environments. The most challenging issue in online algorithms is updating of the model causing tracking failure because of introducing small errors in each update and disturbing the appearance...
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