A convolutional treelets binary feature approach to fast keypoint recognition

Authors:
Chenxia Wu;Jianke Zhu;Jiemi Zhang;Chun Chen;Deng Cai
Affiliations:
College of Computer Science, Zhejiang University, China;College of Computer Science, Zhejiang University, China;College of Computer Science, Zhejiang University, China;College of Computer Science, Zhejiang University, China;College of Computer Science, Zhejiang University, China
Venue:
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Year:
2012

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Abstract

Fast keypoint recognition is essential to many vision tasks. In contrast to the classification-based approaches [1,2], we directly formulate the keypoint recognition as an image patch retrieval problem, which enjoys the merit of finding the matched keypoint and its pose simultaneously. A novel convolutional treelets approach is proposed to effectively extract the binary features from the patches. A corresponding sub-signature-based locality sensitive hashing scheme is employed for the fast approximate nearest neighbor search in patch retrieval. Experiments on both synthetic data and real-world images have shown that our method performs better than state-of-the-art descriptor-based and classification-based approaches.