Image Classification Algorithm Named OCFC Based on Self-supervised Learning
Qihui Shu, Song Liu, Jianwen Wang, Qinghan Lai, Zihan Zhou · 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) · 2020
Deep learning has been successfully applied to computer vision, speech recognition and other domains. In image processing, the CNN model has been relatively mature in the training of labeled data. There are labeled data that need to be manually labeled in supervised learning, but now there are a large number of data without labels or with a small number of labels that need to be processed. We propose an image classification algorithm named OCFC based on self-supervised learning without manual labeling. After image preprocessing, features are extracted by three-layer Convolution Restricted Boltzmann Machine. Then the extracted feature clusters are labeled with pseudo-labels by Fuzzy C-means algorithm. Finally, the CNN model is used to classify and predict other image categories. The self-supervised learning model can be arbitrarily transferred to a shallow model or a deep model. The experimental results show that this method can effectively avoid the complexity of manually extracting features, and the accuracy on the STL-10 dataset reaches 82.7%.