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Efficient logo recognition by local feature groups

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Abstract

This paper presents a method for efficient and scalable logo recognition. Using generalized Hough transform to identify local features that are invariant across images, we can efficiently add spatial information into groups of local features and enhance the discriminative power of local feature. Our method is more flexible and efficient compared with state-of-the-art methods that merge features into groups. To fully exploit the information that different logo images provide, we employ a reference-based image representation scheme to represent training and testing images. Experiments on challenging datasets show that our method is efficient and scalable and achieves state-of-the-art performance.

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Acknowledgments

This work is partly supported by National Natural Science Foundation of China (Grant no. 61379106), the Shandong Provincial Natural Science Foundation (Grant nos. ZR2013FM036, ZR2015FM011), the Open Project Program of the State Key Lab of CAD&CG (Grant no. A1315), Zhejiang University, the Fundamental Research Funds for the Central Universities (Grant nos.14CX02032A, 14CX02031A).

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Authors and Affiliations

  1. College of computer & communication engineering, Qingdao, 266580, Shandong, China

    Yujie Liu, Jun Wang & Zongmin Li

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Hua Li

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  1. Yujie Liu
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  2. Jun Wang
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  3. Zongmin Li
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  4. Hua Li
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Correspondence to Yujie Liu.

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Communicated by Q. Tian.

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Liu, Y., Wang, J., Li, Z. et al. Efficient logo recognition by local feature groups. Multimedia Systems 23, 395–403 (2017). https://doi.org/10.1007/s00530-016-0508-7

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