An Unsupervised Feature Extraction Method based on Multi-granularity Convolution Denoising Autoencoder
Lijuan Cao, Qing Huo Liu, Yun Yang · 2019
In recent years, many cutting-edge research results have emerged in the field of computer vision, especially in the field of image classification. But, researchers are still trying to explore more sophisticated models to further improve the accuracy of image classification. However, many models have failed to achieve satisfactory results due to the complexity of the image and the problems of image noise. Therefore, in order to solve these problems, this paper proposes an unsupervised feature extraction method called multi-granularity convolution denoising autoencoder (MGCDAE). Based on convolutional neural network, the method proposed the concept of multi-granularity convolution kernel to solve the problem of complex image feature extraction. In addition, we introduce denoising autoencoder (DAE) for image noise, which enables our approach to extract more robust features from noise images. The high-level features extracted by the above method are sent to the softmax classifier for classification to evaluate the validity of the feature extraction. Our method has been evaluated on three benchmark data sets, which results show that our approach can extract more discriminative high-level features compared with other existing algorithms.