Abstract
Representing local image patches is a key step in many applications of computer vision, while fast and effective description methods are always required by real-time image processing. Motivated by the fact that quantization compresses information while preserving primary structures, in this paper, we propose to use vector quantization (VQ) on local patch descriptor building. Compared to conventional approaches that compress floating-point features with VQ, we produce local integer descriptors very fast directly based on simple quantization methods. Experimental results on a publicly available dataset show that the present method is efficient both to build and to match. It achieves comparable performance to some typical floating-point and binary descriptors such as SIFT and BRIEF, offering a novel solution to fast local image representation except for bit test created in BRIEF.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China under Grant 61672474 and 41701417, the Fundamental Research Funds for the Central Universities - China University of Geosciences (Wuhan), the Provincial Natural Science Foundation of Hubei Province of China under Grant 2016CFB278 and 2015CFB400, the China Postdoctoral Science Foundation under Grant 2016M602390, and the Open Research Project of Hubei Key Laboratory of Intelligent Geo-Information Processing (KLIGIP1608).
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Tian, T., Yang, F., Zheng, K., Yao, H., Gao, Q. (2018). A Fast Local Image Descriptor Based on Patch Quantization. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2017. Lecture Notes in Computer Science(), vol 10745. Springer, Cham. https://doi.org/10.1007/978-3-319-74521-3_8
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