• An approximation method of the...

Result: An approximation method of the quadratic discriminant function and its application to estimation of high-dimensional distribution

Title:
An approximation method of the quadratic discriminant function and its application to estimation of high-dimensional distribution
Authors:
OMACHI, Shinichiro, OMACHI, Masako, ASO, Hirotomo
Source:
IEICE transactions on information and systems. 90(8):1160-1167
Publisher Information:
Oxford: Oxford University Press, 2007.
Publication Year:
2007
Physical Description:
print, 26 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, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, 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, Signal, bruit, Signal, noise, Détection, estimation, filtrage, égalisation, prédiction, Detection, estimation, filtering, equalization, prediction, Traitement du signal, Signal processing, Reconnaissance des formes, Pattern recognition, Traitement des images, Image processing, Algorithme, Algorithm, Algoritmo, Biométrie, Biometrics, Biometría, Estimation paramètre, Parameter estimation, Estimación parámetro, Evaluation performance, Performance evaluation, Evaluación prestación, Fonction discriminante, Discriminant function, Función discriminante, Fonction quadratique, Quadratic function, Función cuadrática, Méthode approchée, Approximate method, Método aproximado, Précision élevée, High precision, Precisión elevada, Reconnaissance automatique, Automatic recognition, Reconocimiento automático, Reconnaissance caractère, Character recognition, Reconocimiento carácter, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Reconnaissance visage, Face recognition, Temps calcul, Computation time, Tiempo computación, Traitement image, Image processing, Procesamiento imagen, normal mixture, pattern recognition, quadratic discriminant function, simplified quadratic discriminant function, small sample size problem
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Graduate School of Engineering, Tohoku University, Sendai-shi, 980-8579, Japan
Faculty of Science and Technology, Tohoku Bunka Gakuen University, Sendai-shi, 981-8551, Japan
ISSN:
0916-8532
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18993464
Rights:
Copyright 2007 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:
Computer science; theoretical automation; systems

Telecommunications and information theory
Accession Number:
edscal.18993464
Database:
PASCAL Archive

Further Information

In statistical pattern recognition, it is important to estimate the distribution of patterns precisely to achieve high recognition accuracy. In general, precise estimation of the parameters of the distribution requires a great number of sample patterns, especially when the feature vector obtained from the pattern is high-dimensional. For some pattern recognition problems, such as face recognition or character recognition, very high-dimensional feature vectors are necessary and there are always not enough sample patterns for estimating the parameters. In this paper, we focus on estimating the distribution of high-dimensional feature vectors with small number of sample patterns. First, we define a function, called simplified quadratic discriminant function (SQDF). SQDF can be estimated with small number of sample patterns and approximates the quadratic discriminant function (QDF). SQDF has fewer parameters and requires less computational time than QDF. The effectiveness of SQDF is confirmed by three types of experiments. Next, as an application of SQDF, we propose an algorithm for estimating the parameters of the normal mixture. The proposed algorithm is applied to face recognition and character recognition problems which require high-dimensional feature vectors.