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 multiobjects.
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.
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 multiobjects.
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.