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Incremental Learning of Nonparametric Bayesian Mixture Models

Gomes, Ryan and Welling, Max and Perona, Pietro (2008) Incremental Learning of Nonparametric Bayesian Mixture Models. In: IEEE conference on computer vision and pattern recognition. Proceedings – IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE , Piscataway, NJ, pp. 227-234. ISBN 978-1-4244-2242-5.

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Clustering is a fundamental task in many vision applications. To date, most clustering algorithms work in a batch setting and training examples must be gathered in a large group before learning can begin. Here we explore incremental clustering, in which data can arrive continuously. We present a novel incremental model-based clustering algorithm based on nonparametric Bayesian methods, which we call Memory Bounded Variational Dirichlet Process (MB-VDP). The number of clusters are determined flexibly by the data and the approach can be used to automatically discover object categories. The computational requirements required to produce model updates are bounded and do not grow with the amount of data processed. The technique is well suited to very large datasets, and we show that our approach outperforms existing online alternatives for learning nonparametric Bayesian mixture models.

Item Type:Book Section
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Perona, Pietro0000-0002-7583-5809
Additional Information:© 2008 IEEE. This material is based on work supported by the National Science Foundation under grant numbers 0447903 and 0535278, the Office of Naval Research under grant numbers 00014-06-1-0734 and 00014-06-1-0795, and The National Institutes of Health Predoctoral Training in Integrative Neuroscience grant number T32 GM007737.
Funding AgencyGrant Number
Office of Naval Research00014-06-1-0734
Office of Naval Research00014-06-1-0795
NIH Predoctoral Training in Integrative NeuroscienceT32 GM007737
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INSPEC Accession Number10139675
Series Name:Proceedings – IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Record Number:CaltechAUTHORS:20100720-113644123
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:19128
Deposited By: Tony Diaz
Deposited On:30 Jul 2010 22:04
Last Modified:08 Nov 2021 23:49

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