Fine-grained bird species recognition via hierarchical subset learning

ZongYuan Ge, Chris McCool, Conrad Sanderson, Alex Bewley, Zetao Chen, Peter Corke

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

19 Citations (Scopus)


We propose a novel method to improve fine-grained bird species classification based on hierarchical subset learning. We first form a similarity tree where classes with strong visual correlations are grouped into subsets. An expert local classifier with strong discriminative power to distinguish visually similar classes is then learnt for each subset. On the challenging Caltech200-2011 bird dataset we show that using the hierarchical approach with features derived from a deep convolutional neural network leads to the average accuracy improving from 64.5% to 72.7%, a relative improvement of 12.7%.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing (ICIP 2015)
EditorsFabrice Labeau, Jean-Philippe Thiran
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781479983391
ISBN (Print)9781479983407
Publication statusPublished - 9 Dec 2015
Externally publishedYes
EventIEEE International Conference on Image Processing 2015 - Quebec City, Canada
Duration: 27 Sept 201530 Sept 2015
Conference number: 22nd (Proceedings)


ConferenceIEEE International Conference on Image Processing 2015
Abbreviated titleICIP 2015
CityQuebec City
Internet address


  • fine-grained classification
  • subset clustering

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