Incorporating spatial knowledge for classification

Details Incorporating spatial knowledge for classification Incorporating spatial knowledge for classification

2004-08-10

2009-12-15

Perungavoor, Sath V (Department: 2624)

Image analysis

Applications

Biomedical applications

C382S173000, C382S224000

Reexamination Certificate

active

07634120

ABSTRACT:
We propose using different classifiers based on the spatial location of the object. The intuitive idea behind this approach is that several classifiers may learn local concepts better than a “universal” classifier that covers the whole feature space. The use of local classifiers ensures that the objects of a particular class have a higher degree of resemblance within that particular class. The use of local classifiers also results in memory, storage and performance improvements, especially when the classifier is kernel-based. As used herein, the term “kernel-based classifier” refers to a classifier where a mapping function (i.e., the kernel) has been used to map the original training data to a higher dimensional space where the classification task may be easier.

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