Learning Distinctive Local Object Characteristics for 3D Shape Retrieval

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@INPROCEEDINGS{wessel-2008-learning,
      author = {Wessel, Raoul and Baranowski, Rafael and Klein, Reinhard},
      editor = {Deussen, O. and Keim, D. and Saupe, D.},
       pages = {167--178},
       title = {Learning Distinctive Local Object Characteristics for 3D Shape Retrieval},
   booktitle = {Vision, Modeling, and Visualization 2008 (VMV 2008)},
        year = {2008},
       month = oct,
   publisher = {Akademische Verlagsgesellschaft Aka GmbH, Heidelberg},
    abstract = {While supervised learning approaches for 3D shape
                retrieval have been successfully used to incorporate
                human knowledge about object classes based
                on global shape features, the incorporation of local
                features still remains a difficult task. First, it is
                not obvious how to measure the similarity between
                two objects each represented by a set of local features,
                and second, it is not clear how to choose local
                feature scales such that they are most distinctive.
                In this paper, we tackle both of these problems
                and present a supervised learning approach
                that uses arbitrary local features for 3D shape retrieval.
                It avoids the problem of establishing feature
                correspondences and automatically detects discriminating
                feature scales. Our experiments on the
                Princeton Shape Benchmark show that our method
                is superior to state-of-the-art shape retrieval techniques.},
        isbn = {978-3-89838-609-8},
  conference = {The 8th International Fall Workshop Vision, Modeling and Visualisation 2008}
}