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An Efficient Image Categorization Method With Insufficient Training Samples

Image classification is an important part of pattern recognition. With the development of convolutional neural networks (CNNs), many CNN methods are proposed, which have a large number of samples for training, which can have high performance. However, there may exist limited samples in some real-world applications. In order to improve the performance of CNN learning with insufficient samples, this article proposes a new method called the classifier method based on a variational autoencoder (CFVAE), which is comprised of two parts: 1) a standard CNN as a prior classifier and 2) a CNN based on variational autoencoder (VAE) as a posterior classifier. First, the prior classifier is utilized to generate the prior label and information about distributions of latent variables; and the posterior classifier is trained to augment some latent variables from regularized distributions to improve the performance. Second, we also present the uniform objective function of CFVAE and put forward an optimization method based on the stochastic gradient variational Bayes method to solve the objective model. Third, we analyze the feasibility of CFVAE based on Hoeffding’s inequality and Chernoff’s bounding method. This analysis indicates that the latent variables augmentation method based on regularized latent variables distributions can generate samples fitting well with the distribution of data such that the proposed method can improve the performance of CNN with insufficient samples. Finally, the experiments manifest that our proposed CFVAE can provide more accurate performance than state-of-the-art methods.