• Machine learning approach to c...

Treffer: Machine learning approach to color constancy

Title:
Machine learning approach to color constancy
Authors:
AGARWAL, Vivek, GRIBOK, Andrei V, ABIDI, Mongi A
Source:
Neural networks. 20(5):559-563
Publisher Information:
Oxford: Elsevier Science, 2007.
Publication Year:
2007
Physical Description:
print, 1/2 p
Original Material:
INIST-CNRS
Subject Terms:
Electronics, Electronique, Computer science, Informatique, Neurology, Neurologie, 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, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Connexionnisme. Réseaux neuronaux, Connectionism. Neural networks, Processus acquisition, Acquisition process, Proceso adquisición, Algorithme, Algorithm, Algoritmo, Analyse régression, Regression analysis, Análisis regresión, Apprentissage, Learning, Aprendizaje, Autogénération mutuelle, Bootstrapping, Couleur, Color, Evaluation performance, Performance evaluation, Evaluación prestación, Incertitude, Uncertainty, Incertidumbre, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Machine exemple support, Vector support machine, Máquina ejemplo soporte, Optimisation, Optimization, Optimización, Outil, Tool, Herramienta, Régression pseudo orthogonale, Ridge regression, Regresión ridge, Réseau neuronal, Neural network, Red neuronal, Signal vidéo, Video signal, Señal video, Système temps réel, Real time system, Sistema tiempo real, Vision ordinateur, Computer vision, Visión ordenador, Color constancy, Neural networks, Support vector regression
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
400 Central Drive, School of Nuclear Engineering, Purdue University, West Lafayette, IN 47907, United States
BHSAI/MRMC, Attn: MCMR-ZB-T, Building 363 Miller Dr, Fort Detrick, MD 21792-5012, United States
1508 Ferris Hall, Electrical and Computer Engineering, The University of Tennessee, Knoxville, TN 37996, United States
ISSN:
0893-6080
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18980612
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
Accession Number:
edscal.18980612
Database:
PASCAL Archive

Weitere Informationen

A number of machine learning (ML) techniques have recently been proposed to solve color constancy problem in computer vision. Neural networks (NNs) and support vector regression (SVR) in particular, have been shown to outperform many traditional color constancy algorithms. However, neither neural networks nor SVR were compared to simpler regression tools in those studies. In this article, we present results obtained with a linear technique known as ridge regression (RR) and show that it performs better than NNs, SVR, and gray world (GW) algorithm on the same dataset. We also perform uncertainty analysis for NNs, SVR, and RR using bootstrapping and show that ridge regression and SVR are more consistent than neural networks. The shorter training time and single parameter optimization of the proposed approach provides a potential scope for real time video tracking application.