Result: An Unsupervised Feature Selection Dynamic Mixture Model for Motion Segmentation

Title:
An Unsupervised Feature Selection Dynamic Mixture Model for Motion Segmentation
Authors:
THANH MINH NGUYEN, WU, Qingming Jonathan
Source:
IEEE transactions on image processing. 23(3-4):1210-1225
Publisher Information:
New York, NY: Institute of Electrical and Electronics Engineers, 2014.
Publication Year:
2014
Physical Description:
print, 30 ref
Original Material:
INIST-CNRS
Subject Terms:
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, Signal, bruit, Signal, noise, Représentation du signal. Analyse spectrale, Signal representation. Spectral analysis, Détection, estimation, filtrage, égalisation, prédiction, Detection, estimation, filtering, equalization, prediction, Traitement du signal, Signal processing, Traitement des images, Image processing, Traitement image, Image processing, Procesamiento imagen, Algorithme, Algorithm, Algoritmo, Analyse texture, Texture analysis, Análisis textura, Caractéristique temporelle, Time curve, Característica temporal, Classification automatique, Automatic classification, Clasificación automática, Classification non supervisée, Unsupervised classification, Clasificación no supervisada, Classification signal, Signal classification, Espace temps, Space time, Espacio tiempo, Estimation mouvement, Motion estimation, Estimación movimiento, Evaluation performance, Performance evaluation, Evaluación prestación, Extraction caractéristique, Feature extraction, Filtre Kalman, Kalman filter, Filtro Kalman, Modèle dynamique, Dynamic model, Modelo dinámico, Méthode combinatoire, Combinatorial method, Método combinatorio, Précision, Accuracy, Precisión, Robustesse, Robustness, Robustez, Scène naturelle, Natural scene, Escena natural, Segmentation image, Image segmentation, Système dynamique, Dynamical system, Sistema dinámico, Système linéaire, Linear system, Sistema lineal, Théorie mélange, Mixture theory, Teoría mezcla, Traitement signal, Signal processing, Procesamiento señal, Variation temporelle, Time variation, Variación temporal, Feature selection dynamic mixture model (FSDTM), dynamic texture segmentation, linear dynamical system (LDS)
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada
ISSN:
1057-7149
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28496618
Rights:
Copyright 2015 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.28496618
Database:
PASCAL Archive

Further Information

The automatic clustering of time-varying characteristics and phenomena in natural scenes has recently received great attention. While there exist many algorithms for motion segmentation, an important issue arising from these studies concerns that for which attributes of the data should be used to cluster phenomena with a certain repetitiveness in both space and time. It is difficult because there is no knowledge about the labels of the phenomena to guide the search. In this paper, we present a feature selection dynamic mixture model for motion segmentation. The advantage of our method is that it is intuitively appealing, avoiding any combinatorial search, and allowing us to prune the feature set. Numerical experiments on various phenomena are conducted. The performance of the proposed model is compared with that of other motion segmentation algorithms, demonstrating the robustness and accuracy of our method.