Dynamic learning from multiple examples for semantic object segmentation and search
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.
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