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改进的深度卷积网络及在碎纸片拼接中的应用
引用本文: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.
作者姓名:段宝彬  韩立新
作者单位:[1]合肥学院数学与物理系,合肥230601 [2]河海大学计算机与信息学院,南京211100
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。
摘    要:近年来,深度卷积网络在图像识别、语音识别和自然语言处理等领域广泛使用,取得了很好的效果。为解决全部样本均为无标签数据的分类问题,对深度卷积神经网络进行了改进,采用卷积自动编码器学习输入样本的特征,用k-均值聚类器代替深度卷积网络中的分类器,建立了改进的深度卷积网络结构,给出了相应的学习算法,将其用于解决碎纸片拼接问题。实验表明,该方法有效可行,提高了碎纸片拼接的准确性和鲁棒性。

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

Improved convolutional neural networks and its application in stitching of scrapped paper
DUAN Baobin,HAN Lixin.Improved convolutional neural networks and its application in stitching of scrapped paper[J].Computer Engineering and Applications,2014(9):176-181,270.
Authors:DUAN Baobin  HAN Lixin
Affiliation:1.Department of Mathematics and Physics, Hefei University, Hefei 230601, China ;2.College of Computer and Information, Hohai University, Nanjing 211100, China)
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.
Keywords:convolutional neural networks  k-means clustering  stitching of scrapped paper  convolutional auto-encoder  deep learning
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