School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
School of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, China.
Sensors (Basel). 2022 Jan 25;22(3):898. doi: 10.3390/s22030898.
The problem of deep learning network image classification when a large number of image samples are obtained in life and with only a small amount of knowledge annotation, is preliminarily solved in this paper. First, a support vector machine expert labeling system is constructed by using a bag-of-words model to extract image features from a small number of labeled samples. The labels of a large number of unlabeled image samples are automatically annotated by using the constructed SVM expert labeling system. Second, a small number of labeled samples and automatically labeled image samples are combined to form an augmented training set. A deep convolutional neural network model is created by using an augmented training set. Knowledge transfer from SVMs trained with a small number of image samples annotated by artificial knowledge to deep neural network classifiers is implemented in this paper. The problem of overfitting in neural network training with small samples is solved. Finally, the public dataset caltech256 is used for experimental verification and mechanism analysis of the performance of the new method.
本文初步解决了生活中获取大量图像样本，而只有少量知识标注的深度学习网络图像分类问题。首先，利用词袋模型从少量标注样本中提取图像特征，构建支持向量机专家标注系统。然后，利用构建的 SVM 专家标注系统自动标注大量未标注图像样本的标签。其次，将少量标注样本和自动标注图像样本结合形成扩充训练集。利用扩充训练集创建深度卷积神经网络模型。通过人工知识对少量图像样本进行标注训练的 SVM 向深度神经网络分类器进行知识迁移。解决了小样本神经网络训练中过拟合的问题。最后，利用公共数据集 caltech256 对新方法的性能进行实验验证和机制分析。
