A Convolutional Treelets Binary Feature Approach to Fast Keypoint Recognition

Chenxia Wu, Jianke Zhu, Jiemi Zhang, Chun Chen, and Deng Cai

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.

LNCS 7576, p. 368 ff.

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