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Content based image retrieval (CBIR) has become one of the most active research areas in the past few years. Many indexing techniques are based on global feature distributions. However, these global distributions have limited discriminating power because they are unable to capture local image information. In this paper, we propose a content-based image(More)
—A new dynamic classifier fusion method named L-GEM Fusion Method (LFM) for Multiple Classifier Systems (MCSs) is proposed. The localized generalization error upper bound for the neighborhood of a testing sample is calculated and used to estimate the local competence of base classifiers in MCSs. Different from the recent dynamic classifier selection(More)
The training of a multilayer perceptron neural network (MLPNN) concerns the selection of its architecture and the connection weights via the minimization of both the training error and a penalty term. Different penalty terms have been proposed to control the smoothness of the MLPNN for better generalization capability. However, controlling its smoothness(More)