Subspace Morphing Theory for Appearance Based Object
Identification

Zhongfei (Mark) Zhang Rohini K. Srihari
Computer Science Dept. Center of Excellence for Document
Watson School Analysis and Recognition (CEDAR)
State Univerisity of New York State University of New York
Binghamton, NY 13902 Bu
alo, NY 14228
zhongfei@cs.binghamton.edu rohini@cedar.buffalo.edu

Abstract
Object identification techniques have wide applications 
ranging from industry, business, military, law
enforcement, to people's daily life. With the fast development 
in e-business, its applications in internet
related business and commercial models have also been
witnessed to grow fast. This work is motivated to develop 
a new theory for appearance based object identification with 
its applications in different areas.
Although many successful techniques have been proposed in 
certain specific applications, object identification, 
in general, still remains as a dicult and
challenging problem. In appearance based approaches,
typical methods include eigenfaces and the related
subspace analysis, singular value decomposition, Gabor 
wavelet features, neural networks, and shape and
appearance-based probabilistic models. All these methods 
are based on a fundamen tal assumption, i.e., all
the images (both in the model base and to be queried)
are in the same dimensions, so that the feature vectors
are all in the same feature space; if images are provided
with different dimensions, a normalization in scale to
a pre-determined image space must be conducted.
In this research, a theory for appearance based object identication 
called subspace morphing is developed, which allows scale-invariant 
identification of images of objects, and therefore, does not require 
normalization. Theoretical analysis and experimental evaluation 
show that in the situation where images are
provided in different dimensions, which is common in
many applications, subspace morphing theory is superior 
to the existing, normalization-based techniques in
performance.
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