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Deep Hierarchical Representation from Classifying Logo-405

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  • Sujuan Hou
  • Jianwei Lin
  • Shangbo Zhou
  • Maoling Qin
  • Weikuan Jia
  • Yuanjie Zheng

Abstract

We introduce a logo classification mechanism which combines a series of deep representations obtained by fine-tuning convolutional neural network (CNN) architectures and traditional pattern recognition algorithms. In order to evaluate the proposed mechanism, we build a middle-scale logo dataset (named Logo-405) and treat it as a benchmark for logo related research. Our experiments are carried out on both the Logo-405 dataset and the publicly available FlickrLogos-32 dataset. The experimental results demonstrate that the proposed mechanism outperforms two popular ways used for logo classification, including the strategies that integrate hand-crafted features and traditional pattern recognition algorithms and the models which employ deep CNNs.

Suggested Citation

  • Sujuan Hou & Jianwei Lin & Shangbo Zhou & Maoling Qin & Weikuan Jia & Yuanjie Zheng, 2017. "Deep Hierarchical Representation from Classifying Logo-405," Complexity, Hindawi, vol. 2017, pages 1-12, October.
    • Handle: RePEc:hin:complx:3169149
    DOI: 10.1155/2017/3169149
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