Scalable representation and learning for 3D object recognition using shared feature-based view clustering

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In this paper, we present a new scalable 3D object representation and learning method to recognize many objects. Scalability is one of the important issues in object recognition to reduce memory and recognition time. The key idea of scalable representation is to combine a feature sharing concept with view clustering in part-based object representation (especially a CFCM: common frame constellation model). In this representation scheme, we also propose a fully automatic learning method: appearance-based automatic feature clustering and sequential construction of view-tuned CFCMs from labeled multi-views and multi-objects. We applied this learning scheme to 40 objects with 216 training views. Experimental results show the scalable learning results in almost constant recognition performance relative to the number of objects.
Publisher
SPRINGER-VERLAG BERLIN
Issue Date
2006
Language
English
Article Type
Article; Proceedings Paper
Keywords

INTEREST POINT DETECTORS; SCALE

Citation

COMPUTER VISION - ACCV 2006, PT II BOOK SERIES: LECTURE NOTES IN COMPUTER SCIENCE, v.3852, pp.561 - 570

ISSN
0302-9743
URI
http://hdl.handle.net/10203/89625
Appears in Collection
EE-Journal Papers(저널논문)
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