A hierarchical approach to feature extraction and grouping
Foresti, G.L.; Regazzoni, C.
Image Processing, IEEE Transactions on
Volume 9, Issue 6, Jun 2000 Page(s):1056 - 1074
Digital Object Identifier 10.1109/83.846248
Summary:In this paper, the problem of extracting and grouping image
features from complex scenes is solved by a hierarchical approach based
on two main processes: voting and clustering. Voting is performed for
assigning a score to both global and local features. The score
represents the evidential support provided by input data for the
presence of a feature. Clustering aims at individuating a minimal set of
significant local features by grouping together simpler correlated
observations. It is based on a spatial relation between simple
observations on a fixed level, i.e., the definition of a distance in an
appropriate space. As the multilevel structure of the system implies
that input data for an intermediate level are outputs of the lower
level, voting can be seen as a functional representation of the
“part-of” relation between features at different abstraction
levels. The proposed approach has been tested on both synthetic and real
images and compared with other existing feature grouping methods
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