Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusion of handwritten word classifiers
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We propose a novel method for fusing different classifiers outputs. Our approach, called Context Extraction for Local Fusion with Neural Networks (CELF-NN), is a local approach that adapts Artificial Neural Network fusion method to different regions of the feature space. It is based on a novel objective function that combines context identification and multi-algorithm fusion criteria into a joint objective function. This objective function is defined and optimized to produce contexts as compact clusters via unsupervised clustering. Optimization of the objective function also provide an optimal local Neural Network for fusion within each context. Our initial experiments on semantic video indexing have indicated that the proposed fusion approach outperforms all individual classifiers and the global Neural Network fusion method.