Paper Info
Title
Adaptive classifier selection on hierarchical context modeling for robust vision systems
Abstract
This paper proposes a hierarchical image context based adaptable classifier ensemble for efficient visual information processing under uneven illumination environments. In the proposed method, classifier ensemble is constructed in two stages: i) it distinguishes the illumination context of input image in terms of hierarchical context modeling and ii) constructs classifier ensemble using the genetic algorithm (GA). It stores its experiences in terms of the illumination context hieratical manner and derives artificial chromosome so that the context knowledge can be accumulated and used for identification purpose. The proposed method operates in two modes: the learning mode and the action mode. It can improve its performance incrementally using GA in the learning mode. Once sufficient context knowledge is accumulated, the method can operate in real-time. The proposed method has been evaluated in the area of face recognition. The superiority of the proposed method has been shown using international face database FERET.
Year
DOI
Venue
2006
10.1007/11893011_16
KES (3)
Keywords
Field
DocType
classifier ensemble,adaptable classifier ensemble,illumination context,hierarchical image context,illumination context hieratical manner,robust vision system,context knowledge,action mode,adaptive classifier selection,sufficient context knowledge,hierarchical context modeling,face recognition,context model,vision system,real time,genetic algorithm
Facial recognition system,Machine vision,Computer science,Image processing,Context model,Context awareness,Knowledge engineering,Artificial intelligence,Classifier (linguistics),Genetic algorithm,Machine learning
Conference
Volume
ISSN
ISBN
4253
0302-9743
3-540-46542-1
Citations 
PageRank 
References 
0
0.34
14
Authors
5
Name
Order
Citations
PageRank
SongGuo Jin171.11
Eun Sung Jung211.38
Md. Rezaul Bashar3103.95
Mi Young Nam46115.03
Phill Kyu Rhee56024.82