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Logo detection with extendibility and discrimination

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Abstract

Logos are specially designed marks that identify goods, services, and organizations using distinguished characters, graphs, signals, and colors. Identifying logos can facilitate scene understanding, intelligent navigation, and object recognition. Although numerous logo recognition methods have been proposed for printed logos, a few methods have been specifically designed for logos in photos. Furthermore, most recognition methods use codebook-based approaches for the logos in photos. A codebook-based method is concerned with the generation of visual words for all the logo models. When new logos are added, the codebook reconstruction is required if effectiveness is a crucial factor. Moreover, logo detection in natural scenes is difficult because of perspective tilt and non-rigid deformation. Therefore, this study develops an extendable, but discriminating, model-based logo detection method. The proposed logo detection method is based on a support vector machine (SVM) using edge-based histograms of oriented gradient (HOGE) as features through multi-scale sliding window scanning. Thereafter, anti-distortion affine scale invariant feature transform (ASIFT) is used for logo verification with constraints on the ASIFT matching pairs and neighbors. The experimental results using the public Flickr-Logo database confirm that the proposed method has a higher retrieval and precision accuracy compared to existing model-based methods.

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Acknowledgments

The authors would like to thank S. Romberg, L.G. Pueyo, R. Lienhart and R. van Zwol for providing Flickr-Logos dataset and the anonymous reviewers for the valuable and insightful comments on the earlier version of this manuscript.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan

    Kuo-Wei Li & Shu-Yuan Chen

  2. School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China

    Songzhi Su, Hongbo Zhang & Shaozi Li

  3. Fujian Key Laboratory of the Brain-like Intelligent Systems, Xiamen University, Xiamen, China

    Songzhi Su, Hongbo Zhang & Shaozi Li

  4. Department of Computer Science and Information Engineering, Chien Hsin University of Science and Technology, Jhongli City, Taiwan

    Der-Jyh Duh

Authors
  1. Kuo-Wei Li
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  2. Shu-Yuan Chen
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  3. Songzhi Su
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  4. Der-Jyh Duh
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  5. Hongbo Zhang
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  6. Shaozi Li
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Corresponding authors

Correspondence to Shu-Yuan Chen or Songzhi Su.

Additional information

This work was supported by the National Science Council of Taiwan under Grant NSC-101-2221-E-155-060 and National Nature Science Foundation of China under Grant 61202143.

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Li, KW., Chen, SY., Su, S. et al. Logo detection with extendibility and discrimination. Multimed Tools Appl 72, 1285–1310 (2014). https://doi.org/10.1007/s11042-013-1449-1

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  • DOI: https://doi.org/10.1007/s11042-013-1449-1

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