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Multimedia Tools and Applications

, Volume 78, Issue 24, pp 35179–35193 | Cite as

A sketch recognition method based on transfer deep learning with the fusion of multi-granular sketches

  • Peng ZhaoEmail author
  • Yang Liu
  • Yijuan Lu
  • Benpeng Xu
Article
  • 79 Downloads

Abstract

Most of existing sketch recognition methods focus on the contour/shape of whole sketches. They ignore different granularities of sketches during sketching. Stroke sequences of sketches often demonstrate the change of various granularities. In the progress of sketching, a coarser-grained contour gradually changes to a finer-grained object. Different granularities of sketch imply different levels of semantic information and play different roles in sketch recognition. In this paper, a transfer-deep-learning-based sketch recognition method--“sketch-transfer-net” is proposed. Sketch-transfer-net designs a novel fine-tuning strategy to use different granular sketches to fine-tune different layers of neural network. The extensive comparative experiments show that the proposed sketch-transfer-net can capture descriptive information of various granular sketches and therefore improve the performance of sketch recognition. In addition, the novel fine-turning strategy could weaken the negative effect in transfer learning and enable CNNs to be well trained on small sketch datasets.

Keywords

Sketch recognition Deep learning Transfer learning 

Notes

Acknowledgments

This work is supported by National Natural Science Foundation of China (Grant Nos. 61602004) to Dr. Zhao, Natural Science Foundation of the Education Department of Anhui Province (Grant No. KJ2016A041, KJ2017A011), and in part supported by NSF CNS-1305302 and Texas State Research Enhancement Program grant to Dr. Lu.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyAnhui UniversityHefeiChina
  2. 2.Key Laboratory of Intelligent Computing and Signal Processing of Ministry of EducationAnhui UniversityHefeiChina
  3. 3.Department of Computer ScienceTexas State UniversitySan MarcosUSA

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