Deep Hierarchical Representation from Classifying Logo-405

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

Complexity, 2017, vol. 2017, 1-12

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.

Date: 2017
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/8503/2017/3169149.pdf (application/pdf)
http://downloads.hindawi.com/journals/8503/2017/3169149.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:3169149

DOI: 10.1155/2017/3169149

Access Statistics for this article

More articles in Complexity from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().