Learning features and parts for fine-grained recognition

Jonathan Krause, Timnit Gebru, Jia Deng, Li Jia Li, Fei Fei Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

100 Scopus citations


This paper addresses the problem of fine-grained recognition: recognizing subordinate categories such as bird species, car models, or dog breeds. We focus on two major challenges: learning expressive appearance descriptors and localizing discriminative parts. To this end, we propose an object representation that detects important parts and describes fine grained appearances. The part detectors are learned in a fully unsupervised manner, based on the insight that images with similar poses can be automatically discovered for fine-grained classes in the same domain. The appearance descriptors are learned using a convolutional neural network. Our approach requires only image level class labels, without any use of part annotations or segmentation masks, which may be costly to obtain. We show experimentally that combining these two insights is an effective strategy for fine-grained recognition.

Original languageEnglish (US)
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781479952083
StatePublished - Dec 4 2014
Externally publishedYes
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
Duration: Aug 24 2014Aug 28 2014

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Other22nd International Conference on Pattern Recognition, ICPR 2014

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


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