Visual grouping and object recognition
Malik, J.
Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA;
This paper appears in: Image Analysis and Processing, 2001. Proceedings. 11th International Conference on
Publication Date: 26-28 Sep 2001
On page(s): 612-621
Meeting Date: 09/26/2001 - 09/28/2001
Location: Palermo, Italy
ISBN: 0-7695-1183-X
References Cited: 22
INSPEC Accession Number: 7146217
Digital Object Identifier: 10.1109/ICIAP.2001.957078
Current Version Published: 2002-08-07
Abstract
We develop a two-stage framework for parsing and understanding
images, a process of image segmentation grouping pixels to form regions
of coherent color and texture, and a process of recognition - comparing
assemblies of such regions, hypothesized to correspond to a single
object, with views of stored prototypes. We treat segmenting images into
regions as an optimization problem: partition the image into regions
such that there is high similarity within a region and low similarity
across regions. This is formalized as the minimization of the normalized
cut between regions. Using ideas from spectral graph theory, the
minimization can be set as an eigenvalue problem. Visual attributes such
as color, texture, contour and motion are encoded in this framework by
suitable specification of graph edge weights. The recognition problem
requires us to compare assemblies of image regions with previously
stored proto-typical views of known objects. We have devised a novel
algorithm for shape matching based on a relationship descriptor called
the shape context. This enables us to compute similarity measures
between shapes which, together with similarity measures for texture and
color, can be used for object recognition. The shape matching algorithm
has yielded excellent results on a variety of different 2D and 3D
recognition problems
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