Advances in fuzzy integration for pattern recognition
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
Fusion of handwritten word classifiers
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary Pursuit and Its Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Bayesian Similarity Measure for Direct Image Matching
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Journal of Cognitive Neuroscience
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust coding schemes for indexing and retrieval from large face databases
IEEE Transactions on Image Processing
Cascade of fusion for adaptive classifier combination using context-awareness
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Adaptive classifier selection on hierarchical context modeling for robust vision systems
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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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.