Object-based image labeling through learning by example and multi-level segmentation
Date
2003-06Author
Xu, Y.
Duygulu, P.
Saber, Eli
Tekalp, A.
Yarman-Vural, F.
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Show full item recordAbstract
We propose a system that employs low-level image
segmentation followed by color and two-dimensional (2-D) shape
matching to automatically group those low-level segments into objects
based on their similarity to a set of example object templates
presented by the user. A hierarchical content tree data structure
is used for each database image to store matching combinations
of low-level regions as objects. The system automatically initializes
the content tree with only “elementary nodes” representing
homogeneous low-level regions. The “learning” phase refers to labeling
of combinations of low-level regions that have resulted in
successful color and/or 2-D shape matches with the example template(
s). These combinations are labeled as “object nodes” in the
hierarchical content tree. Once learning is performed, the speed of
second-time retrieval of learned objects in the database increases
significantly. The learning step can be performed off-line provided
that example objects are given in the form of user interest profiles.
Experimental results are presented to demonstrate the effectiveness
of the proposed system with hierarchical content tree representation
and learning by color and 2-D shape matching on collections
of car and face images.
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