基于深度置信网络的图像分类方法研究

项目名称： 基于深度置信网络的图像分类方法研究

项目编号： No.61300155

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 周树森

作者单位： 鲁东大学

项目金额： 23万元

中文摘要： 随着可视化数据分析和理解的需求越来越大，图像分类的研究日显重要。最新在由多个神经网络的隐藏层组成的深层架构上的研究成果表明，基于深层架构的深度学习方法的性能还有很大的提升空间。本项目基于经典的深度学习方法- - 深度置信网络，研究图像分类问题。研究的主要内容包括，1) 基于前期的研究成果- - 区分深度置信网络方法，优化深层架构，改进训练方法，研究迭代深度置信网络方法；2) 将区分深度置信网络的抽象能力和模糊集的区分能力相结合，研究模糊深度置信网络方法；3) 将区分深度置信网络方法应用到手写中文识别中，将深层架构的抽象能力和指数损失函数的分类能力相结合，使用深层架构进行粗分类，然后使用改进的二次分类函数进行细分类。通过研究，进一步提升深层架构的图像分类能力，提高手写中文识别的正确率。本项目研究对探索深度学习方法在图像分类和手写中文识别中的应用具有重要意义。

中文关键词： 图像分类；手写识别；深度置信网络；深度学习；

英文摘要： With the rapid development of visual content analysis and understanding, image classification has attracted growing attentions. The new research on deep architecture, which composed of many hidden layers of neural networks, argues that the deep learning methods which based on deep architecture have the potential to improve the performance. This project studies the image classification problems based on classical deep learning method, deep belief networks. The main content of the study including, 1) iterative deep belief networks are proposed based on previous research results, discriminate deep belief networks, which optimizes the deep architechture and modifys the training methods at the same time. 2) fuzzy deep belief networks are proposed, which inherits the powerful abstraction ability of deep architecture and powerful fuzzy classification ability of fuzzy sets. 3) apply discriminate deep belief networks in handwritten Chinese character recognition mission, which integrates the abstraction ability of deep learning method and discriminative ability of exponential loss function, uses deep architecture for coarse classification and modified quadratic discriminant function for fine classification. We can improve the image classification ability of deep architecture continually, and improve the handwriting Chin

英文关键词： Image Classification；Handwriting Recognition；Deep Belief Networks；Deep Learning；