Abstract
This paper addresses a novel method of classifier combination for efficient object recognition using data context-awareness called “Adaptable Classifier Combination (ACC)”. The proposed method tries to distinguish the context category of input image data and decides the classifier combination structure accordingly by Genetic algorithm. It stores its experiences in terms of the data context category and the evolved artificial chromosome so that the evolutionary knowledge can be used later. The proposed method has been evaluated in the area of face recognition. Most previous face recognition schemes define their system structures at the design phases, and the structures are not adaptive during operation. Such approaches usually show vulnerability under varying illumination environment. Data context-awareness, modeling and identification of input data as data context categories, is carried out using SOM(Self Organized Map). The face data context are described based on the image attributes of light direction and brightness. The proposed scheme can adapt itself to an input data in real-time by identifying the data context category and previously derived chromosome. The superiority of the proposed system is shown using four data sets: Inha, FERET and Yale DB.
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Nam, M.Y., Rhee, P.K. (2005). Adaptive Classifier Combination for Visual Information Processing Using Data Context-Awareness. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds) Advances in Intelligent Data Analysis VI. IDA 2005. Lecture Notes in Computer Science, vol 3646. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552253_24
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DOI: https://doi.org/10.1007/11552253_24
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