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Image Classification Systems Based On CNN Based IC and Light-Weight Classifier

机译:基于基于CNN的IC和轻量级分类器的图像分类系统

摘要

Image classification system contains a CNN based IC configured for extracting features out of input data by performing convolution operations using filter coefficients of ordered convolutional layers and a classifier IC configured for classifying the input data using reduced set of the extracted features based on a light-weight classifier. Light-weight classifier is derived by: training filter coefficients of the ordered convolutional layers using a dataset containing N labeled data, the trained filter coefficients are for the CNN based IC; outputting respective extracted features of the N labeled data after performing convolution operations of ordered convolutional layers using the trained filter coefficients, each labeled data contains X features; creating the reduced set of the extracted features by eliminating those of the X features that contain zeros in at least M of the N labeled data; and adjusting M until the light-weight classifier achieves satisfactory results using the reduced set.
机译:图像分类系统包括基于CNN的IC,该IC配置为通过使用有序卷积层的滤波器系数执行卷积运算从输入数据中提取特征;以及分类器IC,其配置为基于轻量使用减少的提取特征集对输入数据进行分类分类器。轻量级分类器的得出方法是:使用包含N个标记数据的数据集训练有序卷积层的滤波器系数,训练后的滤波器系数用于基于CNN的IC;在使用训练过的滤波器系数对有序卷积层进行卷积运算之后,输出N个标记数据的各个提取特征,每个标记数据包含X个特征;通过消除在至少N个标记数据中的M个中包含零的X个特征来创建提取的特征的精简集合;调整M直到使用分类缩减集使轻量级分类器获得满意的结果。

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