Subset feature learning for fine-grained category classification

ZongYuan Ge, Christopher McCool, Conrad Sanderson, Peter Corke

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

24 Citations (Scopus)


Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images. We propose a learning system which first clusters visually similar classes and then learns deep convolutional neural network features specific to each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset show that the proposed method outperforms recent fine-grained categorisation methods under the most difficult setting: no bounding boxes are presented at test time. It achieves a mean accuracy of 77.5%, compared to the previous best performance of 73.2%. We also show that progressive transfer learning allows us to first learn domain-generic features (for bird classification) which can then be adapted to specific set of bird classes, yielding improvements in accuracy.

Original languageEnglish
Title of host publication2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2015)
EditorsKristen Grauman, Erik Learned-Miller, Antonio Torralba, Andrew Zisserman
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages7
ISBN (Print)9781467367608
Publication statusPublished - 19 Oct 2015
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition Workshops 2015 - Boston, United States of America
Duration: 11 Jun 201512 Jun 2015


ConferenceIEEE Conference on Computer Vision and Pattern Recognition Workshops 2015
Abbreviated titleCVPRW 2015
CountryUnited States of America
Internet address


  • Accuracy
  • Australia
  • Birds
  • Feature extraction
  • Learning systems
  • Neural networks
  • Training

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