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  Unsupervised Object Discovery: A Comparison

Tuytelaars, T., Lampert, C., Blaschko, M., & Buntine, W. (2010). Unsupervised Object Discovery: A Comparison. International Journal of Computer Vision, 88(2), 284-302. doi:10.1007/s11263-009-0271-8.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BF8C-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-75C9-8
Genre: Journal Article

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Tuytelaars, T, Author
Lampert, CH1, 2, Author              
Blaschko, MB1, 2, Author              
Buntine, W, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: The goal of this paper is to evaluate and compare models and methods for learning to recognize basic entities in images in an unsupervised setting. In other words, we want to discover the objects present in the images by analyzing unlabeled data and searching for re-occurring patterns. We experiment with various baseline methods, methods based on latent variable models, as well as spectral clustering methods. The results are presented and compared both on subsets of Caltech256 and MSRC2, data sets that are larger and more challenging and that include more object classes than what has previously been reported in the literature. A rigorous framework for evaluating unsupervised object discovery methods is proposed.

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 Dates: 2010-06
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1007/s11263-009-0271-8
BibTex Citekey: 5965
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Title: International Journal of Computer Vision
  Other : Int. J. Comput. Vis.
Source Genre: Journal
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Publ. Info: Hingham, Mass. : Kluwer Academic Publishers
Pages: - Volume / Issue: 88 (2) Sequence Number: - Start / End Page: 284 - 302 Identifier: ISSN: 0920-5691
CoNE: https://pure.mpg.de/cone/journals/resource/954925564668