Scalable representation for 3D object recognition using feature sharing and view clustering

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Scalability is an important issue in object recognition as it reduces database storage and recognition time. In this paper, we propose a new scalable 3D object representation and a learning method to recognize many everyday objects. The key proposal for scalable object representation is to combine the concept of feature sharing with multi-view clustering in part-based object representation, in particular a common-frame constellation model (CFCM). In this representation scheme, we also propose a fully automatic learning method: appearance-based automatic feature clustering and sequential construction of clustered CFCMs from labeled multi-views and multiple objects. We evaluated the scalability of the proposed method to COIL-100 DB and applied the learning scheme to 112 objects with 620 training views. Experimental results show the scalable learning results in almost constant recognition performance relative to the number of objects. (C) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2008-02
Language
English
Article Type
Article
Keywords

INTEREST POINT DETECTORS; SCALE; SYMMETRY

Citation

PATTERN RECOGNITION, v.41, pp.754 - 773

ISSN
0031-3203
DOI
10.1016/j.patcog.2007.03.018
URI
http://hdl.handle.net/10203/20779
Appears in Collection
EE-Journal Papers(저널논문)
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