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Logo Recognition via Improved Topological Constraint

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Book cover MultiMedia Modeling (MMM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9516))

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Abstract

Real-world logo recognition is challenging mainly due to various viewpoints and different lighting conditions. Currently, the most popular approaches are usually based on bag-of-words model due to their good performance. However, their shortcomings lie in two main aspects: (1) wrong recognition results caused by mismatching of keypoints. (2) high computational complexity and extra noise caused by a large number of keypoints which are irrelevant to the target logo. To address these two problems, we propose a new approach which combines feature selection and topological constraint for logo recognition. Firstly, feature selection is applied to filter out most of the irrelevant keypoints. Secondly, an improved topological constraint, which considers the relative position between a keypoint and its neighboring points, is proposed to reduce the number of mismatched keypoints. It is proven in this paper that the proposed constraint can remove the keypoints which are not on the same planar surface with the others from the k nearest neighbors of a keypoint. This property is very important to logo recognition because logos are planar objects in real world. The proposed approach is evaluated on two challenging logo recognition benchmarks, FlickrLogos-32 and FlickrLogos-27, and the experimental results show its effectiveness compared to other popular methods.

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Notes

  1. 1.

    two keypoints are matched if they are quantized into the same visual word.

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Acknowledgements

This work was supported by National Natural Science Foundation of China under Grants 61371128 and 61532005, and National Hi-Tech Research and Development Program of China (863 Program) under Grants 2014AA015102 and 2012AA012503.

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Authors and Affiliations

  1. Institute of Computer Science and Technology, Peking University, Beijing, 100871, China

    Panpan Tang & Yuxin Peng

Authors
  1. Panpan Tang
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  2. Yuxin Peng
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Corresponding author

Correspondence to Yuxin Peng .

Editor information

Editors and Affiliations

  1. University of Texas at San Antonio, San Antonio, USA

    Qi Tian

  2. Dept. of Information Engineering, University of Trento, Povo, Trento, Italy

    Nicu Sebe

  3. EECS, University of Central Florida, Orlando, Florida, USA

    Guo-Jun Qi

  4. EURECOM, Sophia-Antipolis, France

    Benoit Huet

  5. Hefei University of Technology, Hefei, Anhui, China

    Richang Hong

  6. School of Computing and Information, Hefei University of Technology, Hefei, Anhui, China

    Xueliang Liu

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Tang, P., Peng, Y. (2016). Logo Recognition via Improved Topological Constraint. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_13

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  • DOI: https://doi.org/10.1007/978-3-319-27671-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27670-0

  • Online ISBN: 978-3-319-27671-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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