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Carnegie Mellon Robotics Institute

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Abstract

We present a discriminative shape-based algorithm for object category localization and recognition. Our method learns object models in a weakly-supervised fashion, without requiring the specification of object locations nor pixel masks in the training data. We represent object models as cliques of fully-interconnected parts, exploiting only the pairwise geometric relationships between them. The use of pairwise relationships enables our algorithm to successfully overcome several problems that are common to previously-published methods. Even though our algorithm can easily incorporate local appearance information from richer features, we purposefully do not use them in order to demonstrate that simple geometric relationships can match (or exceed) the performance of state-of-the-art object recognition algorithms.

Notes

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

Text Reference

Marius Leordeanu, Martial Hebert, and Rahul Sukthankar, "Beyond Local Appearance: Category Recognition from Pairwise Interactions of Simple Features," Proceedings of CVPR, June, 2007.

BibTeX Reference

@inproceedings{Leordeanu_2007_5759,
   author = "Marius Leordeanu and Martial Hebert and Rahul Sukthankar",
   title = "Beyond Local Appearance: Category Recognition from Pairwise Interactions of Simple Features",
   booktitle = "Proceedings of CVPR",
   month = "June",
   year = "2007",
}

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