DocumentCode :
594960
Title :
Image annotation using adapted Gaussian mixture model
Author :
Tsuboshita, Yukihiro ; Kato, Nei ; Fukui, M. ; Okada, Masayuki
Author_Institution :
Res. & Technol. Group, Fuji Xerox Co., Ltd., Yokohama, Japan
fYear :
2012
fDate :
11-15 Nov. 2012
Firstpage :
1346
Lastpage :
1350
Abstract :
In this paper, an automatic image annotation (AIA) method using Gaussian mixture model (GMM) is discussed. Supervised multiclass labeling (SML), which is a notable AIA method using GMM, has a problem of low annotation performances of labels that have a few training samples because of over fitting. In the present study, we propose to introduce a cross entropy based constraint into SML. According to the proposed method, while probabilistic models of labels are trained independently as is the case with SML, the optimization of whole probabilistic models is achieved, and therefore over fitting is suppressed. As the result of extensive evaluation tests, the proposed method obtained the best annotation performance in existing parametric methods of AIA.
Keywords :
Gaussian processes; entropy; image processing; learning (artificial intelligence); probability; AIA method; GMM; SML; adapted Gaussian mixture model; automatic image annotation method; extensive evaluation tests; parametric methods; probabilistic models; supervised multiclass labeling; Entropy; Fitting; Machine learning; Parametric statistics; Probabilistic logic; Training; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
ISSN :
1051-4651
Print_ISBN :
978-1-4673-2216-4
Type :
conf
Filename :
6460389
Link To Document :
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