@article{ ISI:000223379500004,
Author = {Xu, YW and Saber, E and Tekalp, AM},
Title = {{Dynamic learning from multiple examples for semantic object segmentation
   and search}},
Journal = {{COMPUTER VISION AND IMAGE UNDERSTANDING}},
Year = {{2004}},
Volume = {{95}},
Number = {{3}},
Pages = {{334-353}},
Month = {{SEP}},
Abstract = {{We present a novel ``dynamic learning{''} approach for an intelligent
   image database system to automatically improve object segmentation and
   labeling without user intervention, as new examples become available,
   for object-based indexing. The proposed approach is an extension of our
   earlier work on ``learning by example,{''} which addressed labeling of
   similar objects in a set of database images based on a single example.
   The proposed dynamic learning procedure utilizes multiple example object
   templates to improve the accuracy of existing object segmentations and
   labels. Multiple example templates may be images of the same object from
   different viewing angles, or images of related objects. This paper also
   introduces a new shape similarity metric called normalized area of
   symmetric differences (NASD), which has desired properties for use in
   the proposed ``dynamic learning{''} scheme, and is more robust against
   boundary noise that results from automatic image segmentation.
   Performance of the dynamic learning procedures has been demonstrated by
   experimental results. (C) 2004 Elsevier Inc. All rights reserved.}},
DOI = {{10.1016/j.cviu.2004.04.003}},
ISSN = {{1077-3142}},
Unique-ID = {{ISI:000223379500004}},
}

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