【作者】 张毅；

【导师】 王凯东； 曹玉俊；

【作者基本信息】 西安电子科技大学 ， 工程硕士（专业学位）， 2015， 硕士

【摘要】 随着大数据产业的蓬勃发展,人们通过互联网每天都会产生大量的数据信息,信息的增长正处在一种井喷式的发展方式阶段。那么在这样海量数据的背景之下,我们如何快速获取信息,得到有用的信息就变得尤为重要了。对于传统的文本信息,我们可以利用关键字的查找机制,但是对于图片信息来说,传统的关键字方法已经不能适用。伴随着近几年深度学习的发展,特别是卷积神经网络在计算机视觉领域的广泛应用,图像信息的检索问题开始得到解决,并在快速发展之中。本文提出了一种通过深度卷积神经网络的方法,将图片信息映射到特征空间的方法。利用在特征空间中的图片向量信息,对图片向量进行多分类的问题。本文的创新点在于在深度卷积神经网络的基础之上,利用三元组损失函数,以探究数据分布对图像表达的影响,以寻求保证模糊类内区别,清晰类间差别为目标的最佳的样本组合方式。比较不同损失函数之间的差异。CAFFE框架作为当前主流的深度学习框架,其在工业界以及学术界得到了广泛应用。其优点为方便模型定义,数据引擎通用,论文使用多等优点。在本文中,使用了CAFFE框架进行了模型搭建,并在框架基础之上完成了本文提到的创新点内容的代码编写。本文利用了随机梯度下降的过程,在一个分组之中进行了数据的重组,在特征空间中,寻求目标样本的最大最小距离样本,合理规避样本间的固有相似性。在试验之中,利用了MNIST手写体数据集,其特点是容易分类,因而利用网络进行了2维特征的抽取,进行了直观性的数据分布展示。最后,文章之中还利用了阿里淘宝商品的数据集,进行图像分类,以达到不错的效果。对于数据集的评测,本文采用了ROC曲线以及AUC指标作为评测体系,对不同模型进行了量化的评测。而且对MNIST数据集进行了直观的特征空间分布展示。更多还原

【Abstract】 With the rapid development of large data industry, people will have a lot of data every day via the Internet, the information is in a growth spurt in the development stage. So in the context of such a huge amount of data, how to quickly get the information we obtain useful information becomes particularly important. For the traditional text information, we can use the keyword lookup mechanism, but for image information, the traditional methods have not apply keywords. With the development in recent years, the deep learning, especially convolution neural network is widely used in the field of computer vision, image information retrieval problems began to be solved, and in the fast developing.This thesis presents a neural network by the deep of convolution method, the map image information to a method of feature space. The use of images in the feature space vector information, multi-vector classification of image problems. The innovation of this thesis is based on the deep of convolution neural network above, use triples loss function, in order to explore data distribution on the image expression, to seek within the warranty fuzzy class distinction clear class difference between the best goal The sample combinations. Compare the differences between the different loss functions. CAFFE frame as the current mainstream deep learning framework, which in industry and academia has been widely used. The advantage is convenience model definition, data engines common used, papers using multiple advantages. In this thesis, the use of CAFFE framework to build a model, and completed mentioned here in innovation content of the code written in the frame basis.In this thesis, a stochastic gradient descent procedure, in a grouping being reorganized data in the feature space, seeking to maximize the minimum distance of the sample target sample, reasonable to avoid the inherent similarity between samples. Among the tests, the use of the MNIST handwritten data sets, which is characterized by easy classification, and thus take advantage of the network of the two-dimensional feature extraction, we conducted a visual display of data distribution. Finally, it is also utilized Ali Taobao commodity dataset for image classification, in order to achieve good results. For the evaluation of the data set, we use the ROC curve and AUC as the evaluation index system, the different models of the quantitative evaluation. And for MNIST datasets show the spatial distribution of intuitive features.更多还原