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Volume 2017, Article ID 3169149, 12 pages
Research Article

Deep Hierarchical Representation from Classifying Logo-405

1School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China
2Institute of Life Sciences at Shandong Normal University, Jinan 250014, China
3School of Computer Science, Chongqing University, Chongqing 400030, China
4Key Laboratory of Intelligent Information Processing at Shandong Normal University, Jinan 250014, China

Correspondence should be addressed to Yuanjie Zheng; moc.liamg@eijnauygnehz

Received 30 June 2017; Accepted 29 August 2017; Published 10 October 2017

Academic Editor: Jia Wu

Copyright © 2017 Sujuan Hou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.