Global optimization
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Toward an OSGi-Based Infrastructure for Context-Aware Applications
IEEE Pervasive Computing
Middleware: Context Management in Heterogeneous, Evolving Ubiquitous Environments
IEEE Distributed Systems Online
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Outlier-Preserving Focus+Context Visualization in Parallel Coordinates
IEEE Transactions on Visualization and Computer Graphics
Classifier Ensembles with a Random Linear Oracle
IEEE Transactions on Knowledge and Data Engineering
Discovering and Exploiting Causal Dependencies for Robust Mobile Context-Aware Recommenders
IEEE Transactions on Knowledge and Data Engineering
Collaborative context determination to support mobile terminal applications
IEEE Wireless Communications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
"Fuzzy" versus "nonfuzzy" in combining classifiers designed by Boosting
IEEE Transactions on Fuzzy Systems
Context modeling based on context quantization with application in wavelet image coding
IEEE Transactions on Image Processing
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In this paper, we propose an online evolutionary Context-Aware classifier ensemble framework for object recognition systems which are adaptive to various environments. The starting point utilizes a context recognizer, a context knowledge base and a classifier ensemble generator to provide optimal solutions for classifier ensemble. Even though our framework is quite general and could be applied to various classification tasks, here we focus on the cases of face recognition. The proposed framework uses an unsupervised learning method to carry out context modeling tasks for various environments. The data for classifier ensemble are assigned to corresponding contexts based on supervised learning and an evolutionary algorithm processes all the information to generate context knowledge for online adaptation. Experimental comparisons with systems based on conventional face recognition algorithms upon four extended benchmark data sets, E-FERET, E-Yale, E-INHA, and our own database showed that the system based on our framework was able to operate in dynamic environments with stable performance which others could not achieve.