Efficient unsupervised learning for localization and detection in object categories

Nicolas Loeff, Himanshu Arora, Alexander Sorokin, David Forsyth

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

Abstract

We describe a novel method for learning templates for recognition and localization of objects drawn from categories. A generative model represents the configuration of multiple object parts with respect to an object coordinate system; these parts in turn generate image features. The complexity of the model in the number of features is low, meaning our model is much more efficient to train than comparative methods. Moreover, a variational approximation is introduced that allows learning to be orders of magnitude faster than previous approaches while incorporating many more features. This results in both accuracy and localization improvements. Our model has been carefully tested on standard datasets; we compare with a number of recent template models. In particular, we demonstrate state-of-the-art results for detection and localization.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference
Pages811-818
Number of pages8
StatePublished - Dec 1 2005
Event2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 - Vancouver, BC, Canada
Duration: Dec 5 2005Dec 8 2005

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other2005 Annual Conference on Neural Information Processing Systems, NIPS 2005
CountryCanada
CityVancouver, BC
Period12/5/0512/8/05

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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  • Cite this

    Loeff, N., Arora, H., Sorokin, A., & Forsyth, D. (2005). Efficient unsupervised learning for localization and detection in object categories. In Advances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference (pp. 811-818). (Advances in Neural Information Processing Systems).