Multidimensional indexing for recognizing visual shapes
Califano, A.
Mohan, R.
Exploratory Comput. Vision Group, IBM Thomas J. Watson Res. Center, Yorktown Heights, NY;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Apr 1994
Volume: 16,
Issue: 4
On page(s): 373-392
ISSN: 0162-8828
References Cited: 59
CODEN: ITPIDJ
INSPEC Accession Number: 4679824
Digital Object Identifier: 10.1109/34.277591
Current Version Published: 2002-08-06
Abstract
This paper introduces an analytical framework for studying some
properties of model acquisition and recognition techniques based on
indexing. The goal is to demonstrate that several problems previously
associated with the approach can be attributed to the low dimensionality
of invariants used. These include limited index selectivity. Excessive
accumulation of votes in the look-up table buckets, and excessive
sensitivity to quantization parameters. Theoretical results demonstrate
that using high-dimensional, highly descriptive global invariants
produces better results in terms of accuracy, false positive
suppression, and computation time. A practical example of
high-dimensional global invariants is introduced and used to implement a
2-D shape acquisition/recognition system. The acquisition/recognition
system is based on a two-step table look-up mechanism. First, local
curve descriptors are obtained by correlating image contour information
at short range. Then, seven-dimensional global invariants are computed
by correlating triplets of local curve descriptors at longer range. This
experimental system is meant to illustrate the behavior of a
high-dimensional indexing scheme. Indeed, its performance shows good
agreement with the analytical model with respect to database size, fault
tolerance, and recognition speed. Model acquisition time is linear to
cubic in the number of object features. Object recognition time is
constant to linear in the number of models in the database and linear to
cubic in the number of features in the image. The system has been tested
extensively. With more than 250 arbitrary shapes in the database.
Unsupervised shape and subpart acquisition is demonstrated
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