Authors:
Marko Rak
;
Tim König
and
Klaus-Dietz Tönnies
Affiliation:
Otto-von-Guericke University, Germany
Keyword(s):
Density Difference, Kernel Density Estimation, Scale Space, Blob Detection, Affine Shape Adaption.
Related
Ontology
Subjects/Areas/Topics:
Density Estimation
;
Kernel Methods
;
Pattern Recognition
;
Similarity and Distance Learning
;
Theory and Methods
Abstract:
Identifying differences among the sample distributions of different observations is an important issue in many
fields ranging from medicine over biology and chemistry to physics. We address this issue, providing a general
framework to detect difference spots of interest in feature space. Such spots occur not only at various
locations, they may also come in various shapes and multiple sizes, even at the same location. We deal with
these challenges in a scale-space detection framework based on the density function difference of the observations.
Our framework is intended for semi-automatic processing, providing human-interpretable interest
spots for further investigation of some kind, e.g., for generating hypotheses about the observations. Such interest
spots carry valuable information, which we outline at a number of classification scenarios from UCI
Machine Learning Repository; namely, classification of benign/malign breast cancer, genuine/forged money
and normal/spondylolisthetic/di
sc-herniated vertebral columns. To this end, we establish a simple decision
rule on top of our framework, which bases on the detected spots. Results indicate state-of-the-art classification
performance, which underpins the importance of the information that is carried by these interest spots.
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