Authors:
M. Jazouli
;
J. Wadsworth
and
P. Matsakis
Affiliation:
School of Computer Science, University of Guelph, Ontario and Canada
Keyword(s):
Force Histograms, Relative Position Descriptors, Image Descriptors, Similitudes, Invariants.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Data Engineering
;
Feature Selection and Extraction
;
Information Retrieval
;
Knowledge Acquisition and Representation
;
Ontologies and the Semantic Web
;
Pattern Recognition
;
Shape Representation
;
Software Engineering
;
Theory and Methods
Abstract:
The histogram of forces is a quantitative representation of the relative position of two image objects. It is an image descriptor, like, e.g., shape descriptors. It is not invariant under similitudes, but can be made invariant under similitudes. These are two desirable properties that have been exploited in many applications. Making the histogram of forces invariant under similitudes is achieved through a procedure called normalization. In this paper, we formalize the concept of normalization, review the existing normalization procedures, introduce new ones, and compare all these procedures through experiments involving over 170,000 histogram computations or normalizations.