Treffer: Multiple description pattern analysis : Robustness to misclassification using local discriminant frame expansions

Title:
Multiple description pattern analysis : Robustness to misclassification using local discriminant frame expansions
Authors:
ASDORNWISED, Widhyakorn, JITAPUNKUL, Somchai
Source:
IEICE transactions on information and systems. 88(10):2296-2307
Publisher Information:
Oxford: Oxford University Press, 2005.
Publication Year:
2005
Physical Description:
print, 36 ref
Original Material:
INIST-CNRS
Subject Terms:
Electronics, Electronique, Computer science, Informatique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie du signal et des communications, Signal and communications theory, Codage, codes, Coding, codes, Traitement du signal, Signal processing, Reconnaissance des formes, Pattern recognition, Télécommunications, Telecommunications, Systèmes, réseaux et services de télécommunications, Systems, networks and services of telecommunications, Transmission et modulation (techniques et équipements), Transmission and modulation (techniques and equipments), Radiorepérage et radionavigation, Radiolocalization and radionavigation, Algorithme apprentissage, Learning algorithm, Algoritmo aprendizaje, Analyse conceptuelle, Conceptual analysis, Análisis conceptual, Analyse forme, Pattern analysis, Análisis forma, Appareil portatif, Portable equipment, Aparato portátil, Apprentissage, Learning, Aprendizaje, Canal multiple, Multiple channel, Canal múltiple, Canal transmission, Transmission channel, Canal transmisión, Classification automatique, Automatic classification, Clasificación automática, Classification forme, Pattern classification, Classification signal, Signal classification, Codage source, Source coding, Complexité algorithme, Algorithm complexity, Complejidad algoritmo, Etat actuel, State of the art, Estado actual, Evaluation performance, Performance evaluation, Evaluación prestación, Imagerie radar, Radar imaging, Radar ouverture synthétique, Synthetic aperture radar, Radar abertura sintética, Radar poursuite, Tracking radar, Radar persecusión, Radar surveillance, Search radar, Radar vigilancia, Reconnaissance automatique, Automatic recognition, Reconocimiento automático, Reconnaissance cible radar, Radar target recognition, Robustesse, Robustness, Robustez, Transmission donnée, Data transmission, Transmisión datos, local discriminant basis, multiple classifier systems, multiple description coding models
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Digital Signal Processing Research Laboratory, Department of Electrical Engineering, Chulalongkom University, Bangkok 10330, Thailand
ISSN:
0916-8532
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17177979
Rights:
Copyright 2005 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Telecommunications and information theory
Accession Number:
edscal.17177979
Database:
PASCAL Archive

Weitere Informationen

In this paper, a source coding model for learning multiple concept descriptions of data is proposed. Our source coding model is based on the concept of transmitting data over multiple channels, called multiple description (MD) coding. In particular, frame expansions have been used in our MD coding models for pattern classification. Using this model, there are several interesting properties within a class of multiple classifier algorithms that share with our proposed scheme. Generalization of the MD view under an extension of local discriminant basis towards the (theory of frames allows the formulation of a generalized class of low-complexity learning algorithms applicable to high-dimensional pattern classification. To evaluate this approach, performance results for automatic target recognition (ATR) are presented for synthetic aperture radar (SAR) images from the MSTAR public release data set. From the experimental results, our approach outperforms state-of-the-art methods such as conditional Gaussian signal model, Adaboost, and ECOC-SVM.