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Automatic class selection and prototyping for 3-D object classification

Raghavendra Rao Donamukkala, Daniel Huber, Anuj Kapuria and Martial Hebert
Conference Paper, Carnegie Mellon University, Proceedings of the 5th International Conference on 3-D Digital Imaging and Modeling (3DIM '05), pp. 64-71, June, 2005

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Abstract

Most research on 3-D object classification and recognition focuses on recognition of objects in 3-D scenes from a small database of known 3-D models. Such an approach does not scale well to large databases of objects and does not generalize well to unknown (but similar) object classification. This paper presents two ideas to address these problems (i) class selection, i.e., grouping similar objects into classes (ii) class prototyping, i.e., exploiting common structure within classes to represent the classes. At run time matching a query against the prototypes is sufficient for classification. This approach will not only reduce the retrieval time but also will help increase the generalizing power of the classification algorithm. Objects are segmented into classes automatically using an agglomerative clustering algorithm. Prototypes from these classes are extracted using one of three class prototyping algorithms. Experimental results demonstrate the effectiveness of the two steps in speeding up the classification process without sacrificing accuracy.

BibTeX Reference
@conference{Donamukkala-2005-9208,
title = {Automatic class selection and prototyping for 3-D object classification},
author = {Raghavendra Rao Donamukkala and Daniel Huber and Anuj Kapuria and Martial Hebert},
booktitle = {Proceedings of the 5th International Conference on 3-D Digital Imaging and Modeling (3DIM '05)},
keyword = {3D classification, 3D computer vision},
publisher = {IEEE Computer Society Press},
school = {Robotics Institute , Carnegie Mellon University},
month = {June},
year = {2005},
pages = {64-71},
address = {Pittsburgh, PA},
}
2017-09-13T10:43:23+00:00