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ON THE CONTRIBUTION OF COMPRESSION TO VISUAL PATTERN RECOGNITION

Topics: Cognitive & Biologically Inspired Vision; Early Vision and Image Representation; Feature Extraction; Neural Networks; Object, Event and Scene Recognition, Retrieval and Indexing; Pattern Recognition in Image Understanding; Recognition and Indexing; Statistical Approach; Structural and Syntactic Approach; Visual Learning

Authors: Gunther Heidemann 1 and Helge Ritter 2

Affiliations: 1 Intelligent Systems Group, University of Stuttgart, Germany ; 2 Neuroinformatics Group, Bielefeld University, Germany

ISBN: 978-989-8111-21-0

Keyword(s): Compression, mutual information, Lempel-Ziv, gzip, bzip2, object recognition, texture, image retrieval.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Early Vision and Image Representation ; Feature Extraction ; Features Extraction ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image and Video Analysis ; Informatics in Control, Automation and Robotics ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Signal Processing, Sensors, Systems Modeling and Control ; Soft Computing ; Statistical Approach ; Structural and Syntactic Approach ; Theory and Methods

Abstract: Most pattern recognition problems are solved by highly task specific algorithms. However, all recognition and classification architectures are related in at least one aspect: They rely on compressed representations of the input. It is therefore an interesting question how much compression itself contributes to the pattern recognition process. The question has been answered by Benedetto et al. (2002) for the domain of text, where a common compression program (gzip ) is capable of language recognition and authorship attribution. The underlying principle is estimating the mutual information from the obtained compression factor. Here we show that compression achieves astonishingly high recognition rates even for far more complex tasks: Visual object recognition, texture classification, and image retrieval. Though, naturally, specialized recognition algorithms still outperform compressors, our results are remarkable, since none of the applied compression programs (gzip , bzip2 ) was ever d esigned to solve this type of tasks. Compression is the only known method that solves such a wide variety of tasks without any modification, data preprocessing, feature extraction, even without parametrization. We conclude that compression can be seen as the “core” of a yet to develop theory of unified pattern recognition. (More)

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Paper citation in several formats:
Heidemann G.; Ritter H. and (2008). ON THE CONTRIBUTION OF COMPRESSION TO VISUAL PATTERN RECOGNITION.In Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008) ISBN 978-989-8111-21-0, pages 83-89. DOI: 10.5220/0001078000830089

@conference{visapp08,
author={Gunther Heidemann and Helge Ritter},
title={ON THE CONTRIBUTION OF COMPRESSION TO VISUAL PATTERN RECOGNITION},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)},
year={2008},
pages={83-89},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001078000830089},
isbn={978-989-8111-21-0},
}

TY - CONF

JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)
TI - ON THE CONTRIBUTION OF COMPRESSION TO VISUAL PATTERN RECOGNITION
SN - 978-989-8111-21-0
AU - Heidemann, G.
AU - Ritter, H.
PY - 2008
SP - 83
EP - 89
DO - 10.5220/0001078000830089

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