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Support Vector Machine (SVM) is an effective classifier for classification task, but a vital shortcoming of SVM is that it needs huge computation for large scale learning tasks. Sample selection is a feasible strategy to overcome the problem. In order to rapidly reduce training samples without sacrificing recognition accuracy, this paper presents a novel sample selection strategy based on subspace...
Children are usually treated differently from adults in many computer vision applications. To classify children from adults by face images in a natural and non-intrusive way, a method using improved bio-inspired features (C1-S) is presented in this paper. To reduce the negative influence of individual differences, active shape model (ASM) is used to extract 58 landmarks for face normalization. Motivated...
Some target tracking occasions often requires to tracking a kind of target, such as human face, automobile and so on. A specific target tracking algorithm based on support vector machine (SVM) and AdaBoost is proposed. Moreover, the characteristic data of SVM is a critical factor to success to detecting target. The method selects part of Harr wavelet characters by AdaBoost as input data of SVM training...
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