Discovering scene categories by information projection and cluster sampling

This paper presents a method for unsupervised scene categorization. Our method aims at two objectives: (1) automatic feature selection for different scene categories. We represent images in a heterogeneous feature space to account for the large variabilities of different scene categories. Then, we u...

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Bibliographic Details
Published in2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition pp. 3455 - 3462
Main Authors Dai, Dengxin, Wu, Tianfu, Zhu, Song-Chun
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2010
Subjects
Bayesian methods
Clustering algorithms
Deformable models
Image sampling
Layout
Sampling methods
Signal sampling
Solid modeling
Space exploration
Stochastic processes
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Summary:This paper presents a method for unsupervised scene categorization. Our method aims at two objectives: (1) automatic feature selection for different scene categories. We represent images in a heterogeneous feature space to account for the large variabilities of different scene categories. Then, we use the information projection strategy to pursue features which are both informative and discriminative, and simultaneously learn a generative model for each category. (2) automatic cluster number selection for the whole image set to be categorized. By treating each image as a vertex in a graph, we formulate unsupervised scene categorization as a graph partition problem under the Bayesian framework. Then, we use a cluster sampling strategy to do the partition (i.e. categorization) in which the cluster number is selected automatically for the globally optimal clustering in terms of maximizing a Bayesian posterior probability. In experiments, we test two datasets, LHI 8 scene categories and MIT 8 scene categories, and obtain state-of-the-art results.
ISBN:1424469848
9781424469840
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2010.5539982