Unsupervised modeling of object categories using link analysis techniques

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.

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Gunhee Kim, Christos Faloutsos, Martial Hebert
• Keywords: Art Techniques, Challenging Data Sets, Computer Vision, CVPR 2008, Graph Mining Techniques, Link Analysis Techniques, Unit Visual Features