Learning collections of part models for object recognition

Ian Endres, Kevin J. Shih, Johnston Jiaa, Derek Hoiem

Research output: Contribution to journalConference articlepeer-review


We propose a method to learn a diverse collection of discriminative parts from object bounding box annotations. Part detectors can be trained and applied individually, which simplifies learning and extension to new features or categories. We apply the parts to object category detection, pooling part detections within bottom-up proposed regions and using a boosted classifier with proposed sigmoid weak learners for scoring. On PASCAL VOC 2010, we evaluate the part detectors' ability to discriminate and localize annotated key points. Our detection system is competitive with the best-existing systems, outperforming other HOG-based detectors on the more deformable categories.

Original languageEnglish (US)
Article number6618970
Pages (from-to)939-946
Number of pages8
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - 2013
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States
Duration: Jun 23 2013Jun 28 2013

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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