关键词: 卷积神经网络, k-均值聚类, 碎纸片拼接, 卷积自动编码器, 深度学习

Abstract: In recent years, deep convolutional networks are widely used in image recognition, speech recognition and natural language processing and other fields, which have achieved very good results. In this paper, in order to solve classification problems with all the samples being unlabeled data, deep convolutional neural network is improved and the corresponding learning algorithm is given，with the k-means clustering device replacing the classifier in deep convolutional network and adopting convolutional auto-encoder. The improved learning algorithm is used to solve the stitching of scrapped paper. The experiments show that, this method is effective and feasible, which improves the accuracy and robustness of the stitching of scrapped paper.

Key words: convolutional neural networks, k-means clustering, stitching of scrapped paper, convolutional auto-encoder, deep learning