Study of CNN Based Classification for Small Specific Datasets

  • Huu Ton LeEmail author
  • Thierry Urruty
  • Marie Beurton-Aimar
  • Thi Phuong Nghiem
  • Hoang Tung Tran
  • Romain Verset
  • Marie Ballere
  • Hien Phuong Lai
  • Muriel Visani
Part of the Studies in Computational Intelligence book series (SCI, volume 769)


Recently, deep learning and particularly, Convolutional Neural Network (CNN), has become predominant in many application fields, including visual image classification. In an applicative context of detecting areas with hazard of dengue fever, we propose a classification framework using deep neural networks on a limited dataset of images showing urban sites. For this purpose, we have to face multiple research issues: (i) small number of training data; (ii) images belonging to multiple classes; (iii) non-mutually exclusive classes. Our framework overcomes those issues by combining different techniques including data augmentation and multi-scale/region-based classification, in order to extract the most discriminative information from the data. Experiment results present our framework performance using several CNN architectures with different parameter sets, without and with transfer learning. Then, we analyze the effect of data augmentation and multiscale region based classification. Finally, we show that adding a classification weighting scheme allows the global framework to obtain more than 90% average precision for our classification task.


CNN Image classification Small and dedicated dataset 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Huu Ton Le
    • 1
    Email author
  • Thierry Urruty
    • 2
  • Marie Beurton-Aimar
    • 3
  • Thi Phuong Nghiem
    • 1
  • Hoang Tung Tran
    • 1
  • Romain Verset
    • 4
  • Marie Ballere
    • 4
  • Hien Phuong Lai
    • 1
    • 5
  • Muriel Visani
    • 6
  1. 1.ICT LabUniversity of Science and Technology of Hanoi (USTH)Cau Giay, HanoiVietnam
  2. 2.XLIM, UMR CNRS 7252, University of PoitiersPoitiersFrance
  3. 3.LaBRI, University of BordeauxBordeauxFrance
  4. 4.ENSEEIHTToulouseFrance
  5. 5.M&S lab, UMI UMMISCO 209, IRDFCSE, Thuyloi UniversityHanoiVietnam
  6. 6.Laboratory L3iUniversity of La RochelleLa RochelleFrance

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