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IMAGE FEATURE LEARNING DEVICE, IMAGE FEATURE LEARNING METHOD, IMAGE FEATURE EXTRACTION DEVICE, IMAGE FEATURE EXTRACTION METHOD, AND PROGRAM
IMAGE FEATURE LEARNING DEVICE, IMAGE FEATURE LEARNING METHOD, IMAGE FEATURE EXTRACTION DEVICE, IMAGE FEATURE EXTRACTION METHOD, AND PROGRAM
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机译:图像特征学习装置,图像特征学习方法,图像特征提取装置,图像特征提取方法以及程序
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摘要
To learn a neural network for extracting a feature of an image high in robustness relative to an image region having no identification force, while minimizing the number of parameters of a pooling layer.SOLUTION: A loss function is represented using the distance between a first feature vector of a first image and a second feature vector of a second image, as fitting images obtained by applying a convolution neural network including a full convolution layer which outputs a feature tensor of an input image by applying convolution to the input image, a weight matrix estimation layer which estimates a weight matrix indicating the weight of each element of the feature tensor, and a pooling layer which extracts a feature vector of the input image based on the feature tensor and the weight matrix. A parameter learning unit 130 learns the parameter of each layer of the convolution neural network, so that a loss function value obtained by calculating the loss function becomes small.SELECTED DRAWING: Figure 2
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