2002
@article{LeS2002,
vgclass = {refpap},
author = {Kyoung-Mi Lee and W. Nick Street},
title = {Incremental feature weight learning and its application to
a shape-based query system},
journal = {Pattern Recognition Letters},
volume = {23},
number = {7},
pages = {865--874},
month = {May},
year = {2002},
url = {http://dx.doi.org/10.1016/S0167-8655(01)00161-1},
abstract = {Similarity between shapes is often measured by computing
the distance between two feature vectors. Unfortunately, the feature
space cannot always capture the notion of similarity in human
perception. So, most current image retrieval systems use weights
measuring the importance of each feature. However, the similarity does
not vary with equal strength or in the same proportion in all
directions in the feature space. In this paper, we present feature
weights based on both clustered objects in the database and on
relevance feedback. We show that using variance information from shape
clusters to guide cluster information for an initial database search
gives better results than using the standard Euclidean distance. To
automatically incorporate a user's need, the proposed shape-based query
system uses an incremental feature weight learning method that refines
prototypes. In contrast to existing image database systems, the system
can learn from user feedback. Indexing and retrieval results are
presented that demonstrate the efficacy of our technique using the
well-known Columbia database.},
}