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Journal Article

Unsupervised Object Discovery: A Comparison

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84037

Lampert,  CH
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent System, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83816

Blaschko,  MB
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-BF8C-1
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.