基于内容的动画短片分类
发布时间：2018-10-24 11:59
【摘要】： 随着互联网技术的迅速发展,网上的多媒体信息也越来越多,特别是近两年来,数码动画继音乐和图片之后异军突起,成为又一种互联网上用来传播信息的常见数字媒体。因此,迫切需要一种技术对动画进行分类,检索和过滤。过去几年中迅速发展的CBIR技术虽然对静态图像取得了满意的效果,但是这些技术并不是针对动画设计的,无法直接用于动画的分类,检索和过滤。 鉴于此,本文尝试在传统CBIR技术的基础上,提出一种用于基于内容的动画短片分类方法。由于现在许多动画被用作广告,对用户来说是一种垃圾信息,因此找到一种检测和过滤这种信息的方法是很有价值的,在本文中,主要按照这两类对动画进行分类。考虑到动画与图片分类的主要不同来自于特征提取,而分类器并不关心其输入的特征向量是来自于动画还是图片,因此本文将重点放在特征的提取和分析上。 本文首先介绍了基于内容的图像检索技术的发展现状、系统构架以及关键技术基础,详细阐述了图像语义特征的提取方法,分析方法以及常用的分类方法,鉴于本文分类目标的特殊性,还介绍了一些其它的特征提取方法,例如图像中文字区域的识别等。 在提取特征的基础上,本文使用互信息量(MI)对不同特征的有效性进行了分析,对提取的不同特征的判别力进行了比较;此外,还分析了将动画整体考虑与将其看作一系列图片考虑时的不同,指出后一种做法的效果较差。 最后,本文使用RBF核的支持向量机(SVM)作为分类器,对特征分析的结果进行了验证,不但比较了单个特征的分类结果,也比较了不同特征的组合的分类结果。最终的分类结果验证了对特征进行分析时的结论,最后最优的特征组合平均错误概率达到了8.28%。
[Abstract]:With the rapid development of Internet technology, there are more and more multimedia information on the Internet, especially in the past two years, digital animation, after music and pictures, has become another common digital media used to spread information on the Internet. Therefore, there is an urgent need for a technology to classify, retrieve and filter animation. The rapid development of CBIR technology in the past few years has achieved satisfactory results for still images, but these techniques are not designed for animation and can not be directly used for animation classification, retrieval and filtering. In view of this, based on the traditional CBIR technology, this paper proposes a method for content-based animation short film classification. Now many animations are used as advertisements, which is a kind of junk information for users, so it is very valuable to find a way to detect and filter this information. In this paper, we classify the animation according to these two kinds of animation. Considering that the main difference between animation and image classification comes from feature extraction and the classifier does not care whether the input feature vector is from animation or picture, this paper focuses on feature extraction and analysis. This paper first introduces the development of content-based image retrieval technology, the system framework and the key technical basis, and describes the image semantic feature extraction methods, analysis methods and common classification methods in detail. In view of the particularity of the classification object in this paper, some other feature extraction methods are also introduced, such as the recognition of the text region in the image, and so on. On the basis of feature extraction, the validity of different features is analyzed by using mutual information quantity (MI), and the discriminant power of different features is compared. The difference between considering animation as a whole and considering it as a series of pictures is also analyzed, and it is pointed out that the effect of the latter method is poor. Finally, the support vector machine (SVM) based on RBF kernel is used as the classifier to verify the results of feature analysis, not only comparing the classification results of individual features, but also comparing the classification results of different feature combinations. The final classification results verify the conclusion of the feature analysis, and the average error probability of the optimal feature combination reaches 8.28%.
【学位授予单位】：上海交通大学
【学位级别】：硕士
【学位授予年份】：2007
【分类号】：TP391.4
本文编号：2291341
[Abstract]:With the rapid development of Internet technology, there are more and more multimedia information on the Internet, especially in the past two years, digital animation, after music and pictures, has become another common digital media used to spread information on the Internet. Therefore, there is an urgent need for a technology to classify, retrieve and filter animation. The rapid development of CBIR technology in the past few years has achieved satisfactory results for still images, but these techniques are not designed for animation and can not be directly used for animation classification, retrieval and filtering. In view of this, based on the traditional CBIR technology, this paper proposes a method for content-based animation short film classification. Now many animations are used as advertisements, which is a kind of junk information for users, so it is very valuable to find a way to detect and filter this information. In this paper, we classify the animation according to these two kinds of animation. Considering that the main difference between animation and image classification comes from feature extraction and the classifier does not care whether the input feature vector is from animation or picture, this paper focuses on feature extraction and analysis. This paper first introduces the development of content-based image retrieval technology, the system framework and the key technical basis, and describes the image semantic feature extraction methods, analysis methods and common classification methods in detail. In view of the particularity of the classification object in this paper, some other feature extraction methods are also introduced, such as the recognition of the text region in the image, and so on. On the basis of feature extraction, the validity of different features is analyzed by using mutual information quantity (MI), and the discriminant power of different features is compared. The difference between considering animation as a whole and considering it as a series of pictures is also analyzed, and it is pointed out that the effect of the latter method is poor. Finally, the support vector machine (SVM) based on RBF kernel is used as the classifier to verify the results of feature analysis, not only comparing the classification results of individual features, but also comparing the classification results of different feature combinations. The final classification results verify the conclusion of the feature analysis, and the average error probability of the optimal feature combination reaches 8.28%.
【学位授予单位】：上海交通大学
【学位级别】：硕士
【学位授予年份】：2007
【分类号】：TP391.4
【引证文献】
中国硕士学位论文全文数据库 前2条
1 仝琳;论摄影技术在动画制作中的重要作用[D];山东师范大学;2010年
2 刘永翔;基于支持向量机的瓦斯突出预测研究[D];太原理工大学;2012年
，本文编号：2291341
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