计算机工程与应用Issue(9)：176-181,270,7.DOI:10.3778/j.issn.1002-8331.1312-0134
改进的深度卷积网络及在碎纸片拼接中的应用
Improved convolutional neural networks and its application in stitching of scrapped paper
摘要
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.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Key words
convolutional neural networks/k-means clustering/stitching of scrapped paper/convolutional auto-encoder/deep learning分类
信息技术与安全科学引用本文复制引用
段宝彬,韩立新..改进的深度卷积网络及在碎纸片拼接中的应用[J].计算机工程与应用,2014,(9):176-181,270,7.基金项目
江苏省高校“青蓝工程”中青年学术带头人培养对象资助项目；合肥学院应用数学重点（建设）学科基金资助。 （）
