The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

We propose an approach for learning visual models of object categories in an unsupervised manner in which we first build a large-scale complex network which captures the interactions of all unit visual features across the entire training set and we infer information, such as which features are in which categories, directly from the graph by using link analysis techniques. The link analysis techniques are based on well-established graph mining techniques used in diverse applications such as WWW, bioinformatics, and social networks. The techniques operate directly on the patterns of connections between features in the graph rather than on statistical properties, e.g., from clustering in feature space. We argue that the resulting techniques are simpler, and we show that they perform similarly or better compared to state of the art techniques on common data sets. We also show results on more challenging data sets than those that have been used in prior work on unsupervised modeling.

Keywords

Computer Vision, Data Mining

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Number of pages: 8

Text Reference

Gunhee Kim, Christos Faloutsos, and Martial Hebert, "Unsupervised Modeling of Object Categories Using Link Analysis Techniques," IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June, 2008.

BibTeX Reference

@inproceedings{Kim_2008_6030,
   author = "Gunhee Kim and Christos Faloutsos and Martial Hebert",
   title = "Unsupervised Modeling of Object Categories Using Link Analysis Techniques",
   booktitle = "IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)",
   month = "June",
   year = "2008",
}

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