文章基本信息

标题：Selecting Discriminative Binary Patterns for a Local Feature
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作者：Yingying Li ; Jieqing Tan ; Jinqin Zhong 等
期刊名称：Cybernetics and Information Technologies
印刷版ISSN：1311-9702
电子版ISSN：1314-4081
出版年度：2015
卷号：15
期号：3
DOI：10.1515/cait-2015-0044
出版社：Bulgarian Academy of Science
摘要：The local descriptors based on a binary pattern feature have state-of-the- art distinctiveness. However, their high dimensionality resists them from matching faster and being used in a low-end device. In this paper we propose an efficient and feasible learning method to select discriminative binary patterns for constructing a compact local descriptor. In the selection, a searching tree with Branch&Bound is used instead of the exhaustive enumeration, in order to avoid tremendous computation in training. New local descriptors are constructed based on the selected patterns. The efficiency of selecting binary patterns has been confirmed by the evaluation of these new local descriptors' performance in experiments of image matching and object recognition.
关键词：Selecting patterns; searching tree; local descriptor; matching; binary ; pattern