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METHOD AND APPARATUS FOR IMAGE CLASSIFICATION WITH JOINT FEATURE ADAPTATION AND CLASSIFIER LEARNING
METHOD AND APPARATUS FOR IMAGE CLASSIFICATION WITH JOINT FEATURE ADAPTATION AND CLASSIFIER LEARNING
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机译:具有联合特征自适应和分类器学习的图像分类方法和装置
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摘要
A technique for improving the performance of image classification systems is proposed which consists of learning an adaptation architecture on top of the input features jointly with linear classifiers, e.g., SVM. This adaptation method is agnostic to the type of input feature and applies either to features built using aggregators, e.g., BoW, FV, or to features obtained from the activations or outputs from DCNN layers. The adaptation architecture may be single (shallow) or multi-layered (deep). This technique achieves a higher performance compared to current state of the art classification systems.
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