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Workshop on Motion and Video Computing, 2002. Proceedings.
Feature selection for video categorization is impractical with existing techniques. In this paper we present a novel algorithm to select a very small subset of image features. We reduce the cardinality of the input data by sorting the individual features by their effectiveness in categorization, and then merging pairwise these features into feature sets of cardinality two. Repeating this sortmerge process several times results in the learning of a small-cardinality, efficient, but highlydoi:10.1109/motion.2002.1182216 fatcat:pmdu2defvbaujc3d4y22vz5xne