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2004

Dynamic learning from multiple examples for semantic object segmentation and search

11 years 8 months ago
Dynamic learning from multiple examples for semantic object segmentation and search
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 segm...
Yaowu Xu, Eli Saber, A. Murat Tekalp
Added 17 Dec 2010
Updated 17 Dec 2010
Type Journal
Year 2004
Where CVIU
Authors Yaowu Xu, Eli Saber, A. Murat Tekalp
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