Adaptive classifier selection on hierarchical context modeling for robust vision systems
Paper
Paper/Presentation Title | Adaptive classifier selection on hierarchical context modeling for robust vision systems |
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Presentation Type | Paper |
Authors | Jin, SongGuo (Author), Jung, Eun Sung (Author), Bashar, Md. Rezaul (Author), Nam, Mi Young (Author) and Rhee, Phill Kyu (Author) |
Editors | Gabrys, Bogdan, Howlett, Robert J. and Jain, Lakhmi C. |
Journal or Proceedings Title | Lecture Notes in Artificial Intelligence (Book series) |
ERA Conference ID | 43436 |
Journal Citation | 4253, pp. 124-134 |
Number of Pages | 11 |
Year | 2006 |
Place of Publication | Berlin / Heidelberg, Germany |
ISBN | 9783540465423 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/11893011_16 |
Web Address (URL) of Paper | http://springerlink.metapress.com/content/v3226g258764l655/fulltext.pdf |
Conference/Event | 10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES 2006) |
International Conference on Knowledge-Based and Intelligent Information and Engineering Systems | |
Event Details | International Conference on Knowledge-Based and Intelligent Information and Engineering Systems KES Rank B B B |
Event Details | 10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES 2006) Parent International Conference on Knowledge-Based and Intelligent Information and Engineering Systems Delivery In person Event Date 09 to end of 11 Oct 2006 Event Location Bournemouth, United Kingdom |
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 |
Keywords | context awareness; face recognition; classifier ensemble; evolvable classifier selection; hierarchical context modeling; genetic algorithm |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
490102. Biological mathematics | |
460306. Image processing | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Inha University, Korea |
Series | Lecture Notes in Computer Science |
Book Title | Knowledge-based intelligent information and engineering systems |
https://research.usq.edu.au/item/9zy2z/adaptive-classifier-selection-on-hierarchical-context-modeling-for-robust-vision-systems
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