A study of Gaussian mixture models of color and texture features for image classification and segmentation

Pattern Recognition

Volumn 39, Issue 4, 2006, Pages 695-706

  Permuter, Haim ^a,d   Francos, Joseph ^b   Jermyn, Ian ^c,e  

^a Stanford University   (United States)

^b BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

^c UNIVERSITY OF NICE SOPHIA ANTIPOLIS   (France)

^d STANFORD UNIVERSITY   (United States)

^e INRIA   (France)

Author keywords

Aerial images; Background model; Color; Decision fusion; Expectation maximization; Gaussian mixture models; Image classification; Image segmentation; k means; Texture

Indexed keywords

AERIAL IMAGES; BACKGROUND MODEL; DECISION FUSION; EXPECTATION MAXIMIZATION; GAUSSIAN MIXTURE MODELS; IMAGE CLASSIFICATION; K-MEANS;

COLOR; DATABASE SYSTEMS; GAUSSIAN NOISE (ELECTRONIC); IMAGE SEGMENTATION; MATHEMATICAL MODELS; TEXTURES;

IMAGE ANALYSIS;

EID: 32044457458     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2005.10.028     Document Type: Article

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