深度学习及其在社会化媒体分祈中的应用研究
发布时间：2018-11-24 11:16
【摘要】：近年来由于移动设备技术及互联网技术的不断发展,极大的方便人们随时随地进行图片拍摄,这就使得以图像为出发点的社交媒体如Flickr、Instagram等开始大量的涌现。如何有效地管理组织这些海量的图像数据,并对社会化媒体中图像进行挖掘分析以促进个体在线交流,提升用户体验,辅助企业做出营销决策成为了研究的热点问题。而深度学习正是能够从海量的数据中进行学习、挖掘的一种机器学习方法。其深度分层结构与人类视觉系统具有深度分层的特点一致,所以深度学习人类符合人类生物学上对图像认知的过程。自2006年深度学习被Hinton提出后就引发了学术界、工业界的研究热潮,已经涌现大量的研究和应用。深度学习强调可学习性的特点,因此它适合于学习具有良好表达力的图像特征,进而满足社会化媒体图像语义学习分类、图像美学质量评价以及以此为基础的社会化媒体分析研究。本文针对以上问题提出了基于深度学习模型的社会化媒体图像语义分类算法和社会化媒体图像美学质量评价算法并进行了应用。本文首先详细介绍了深度学习算法的基本思想、训练方法以及其与传统神经网络的主要异同,并且对几种得到广泛研究应用的深度学习模型作了阐述。其次,描述了深度学习在图像语义分类应用的问题定义及现有的方法,并提出了基于栈式去噪编码器(Stacked denoising Auto-Encoder, SdEA)和基于卷积深度玻尔兹曼机(Convolution Deep Boltzmann Machine, CDBM)的图像语义分类模型。并且用实验证明这两种模型在社会化图像语义分类问题中的有效性。再次,介绍了深度学习在图像美学质量评价中的应用的问题定义,并提出了深度卷积神经网络+sVM分类器的图像美学质量评价模型,用实验验证了其有效性和可行性。最后,总结分析了文章的不足之处,为后续的研究提供方向。
[Abstract]:In recent years, with the continuous development of mobile device technology and Internet technology, it is greatly convenient for people to take pictures at any time and anywhere, which makes social media such as Flickr,Instagram as the starting point to emerge in large numbers. How to effectively manage and organize these massive image data, and how to mine and analyze images in social media to promote individual online communication, improve user experience and assist enterprises to make marketing decisions has become a hot issue. Deep learning is a machine learning method that can learn from massive data. The structure of depth stratification is consistent with that of human visual system, so the deep learning of human is in accordance with the process of image cognition in human biology. Since the deep learning was put forward by Hinton in 2006, it has triggered a research boom in academia and industry, and a large number of research and applications have emerged. Deep learning emphasizes the characteristics of learnability, so it is suitable for learning image features with good expressiveness, thus satisfying the classification of image semantic learning in social media. Image aesthetic quality evaluation and social media analysis based on it. In order to solve the above problems, this paper proposes a social media image semantic classification algorithm based on the in-depth learning model and an algorithm for evaluating the aesthetic quality of social media images. In this paper, the basic idea of depth learning algorithm, training method and its main similarities and differences with traditional neural network are introduced in detail, and several kinds of depth learning models which have been widely studied and applied are described. Secondly, the problem definition and existing methods of depth learning in image semantic classification are described, and a stack denoising encoder (Stacked denoising Auto-Encoder, SdEA) and convolution depth Boltzmann machine (Convolution Deep Boltzmann Machine, are proposed. CDBM) image semantic classification model. Experiments show that the two models are effective in the problem of socialized image semantic classification. Thirdly, the problem definition of the application of depth learning in image aesthetic quality evaluation is introduced, and the evaluation model of image aesthetic quality of sVM classifier based on deep convolution neural network is proposed. The validity and feasibility of the model are verified by experiments. Finally, the paper summarizes and analyzes the shortcomings of the article, and provides the direction for further research.
【学位授予单位】：华北电力大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TP391.41
本文编号：2353487
[Abstract]:In recent years, with the continuous development of mobile device technology and Internet technology, it is greatly convenient for people to take pictures at any time and anywhere, which makes social media such as Flickr,Instagram as the starting point to emerge in large numbers. How to effectively manage and organize these massive image data, and how to mine and analyze images in social media to promote individual online communication, improve user experience and assist enterprises to make marketing decisions has become a hot issue. Deep learning is a machine learning method that can learn from massive data. The structure of depth stratification is consistent with that of human visual system, so the deep learning of human is in accordance with the process of image cognition in human biology. Since the deep learning was put forward by Hinton in 2006, it has triggered a research boom in academia and industry, and a large number of research and applications have emerged. Deep learning emphasizes the characteristics of learnability, so it is suitable for learning image features with good expressiveness, thus satisfying the classification of image semantic learning in social media. Image aesthetic quality evaluation and social media analysis based on it. In order to solve the above problems, this paper proposes a social media image semantic classification algorithm based on the in-depth learning model and an algorithm for evaluating the aesthetic quality of social media images. In this paper, the basic idea of depth learning algorithm, training method and its main similarities and differences with traditional neural network are introduced in detail, and several kinds of depth learning models which have been widely studied and applied are described. Secondly, the problem definition and existing methods of depth learning in image semantic classification are described, and a stack denoising encoder (Stacked denoising Auto-Encoder, SdEA) and convolution depth Boltzmann machine (Convolution Deep Boltzmann Machine, are proposed. CDBM) image semantic classification model. Experiments show that the two models are effective in the problem of socialized image semantic classification. Thirdly, the problem definition of the application of depth learning in image aesthetic quality evaluation is introduced, and the evaluation model of image aesthetic quality of sVM classifier based on deep convolution neural network is proposed. The validity and feasibility of the model are verified by experiments. Finally, the paper summarizes and analyzes the shortcomings of the article, and provides the direction for further research.
【学位授予单位】：华北电力大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TP391.41
【参考文献】
相关期刊论文 前1条
1 高隽;谢昭;张骏;吴克伟;;图像语义分析与理解综述[J];模式识别与人工智能;2010年02期
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