基于多特征组合和SVM的视频内容自动分类算法研究
发布时间：2018-10-25 13:45
【摘要】： 基于内容的视频自动分类是多媒体分析领域中一个重要的研究课题,它为日益增加的视频数据的管理提供了方便,基于内容的视频自动分类作为视频传播控制的一类关键技术在对网络媒体进行有序管理的需求下至关重要。基于视频自动分类技术的应用,媒体网站可以把海量的视频内容进行自动分类,可实现对不良视频信息的自动初步筛选。并且它在VOD和智能HDTV的发展中也发挥着重要的作用。 基于内容的视频分类性能极大地依赖于视频特征的提取和分类模型的选取,本文从对视频内容和视频风格类型的角度出发,提出了基于视觉多特征组合的视频特征提取方法和改进支持向量机(SVM)视频分类算法,实现了对卡通、广告、音乐、新闻和体育这五类最常见的视频自动分类。 首先,在分析现有的视频分类算法的基础上,针对现有算法存在的问题,通过分析五类典型视频在视觉上的差异,本文提出了新的特征表达方案即多特征组合模型,从编辑、颜色、纹理、运动四个方面提取了共十五种特征来构成新的视觉特征组合模型,所选用的特征空间增强了同类样本分布的紧致性和异类样本分布的差异性。在有效性和区分度上达到了满意的效果。 在选择并提取了合适的特征后,针对目前统计方法中存在的通过小样本集很难设计有效分类器的问题,本文提出了基于支持向量机的视频内容自动分类算法。并对分类器判决策略方法进行了改进,提出了基于动态阈值边界向量抽取方法的一对多决策方法;基于二次预测机制的一对一决策方法;和基于交叉验证概率误差检验机制的有向无环图决策方法。 通过仿真实验结果说明:本文算法在特征选择方面增强了五类视频的区分能力,而且降低了单一特征的计算复杂度;其次,提高了SVM分类器的多视频分类的性能;最后,与相关算法进行了对比实验,证明了本文算法在分类正确率方面性能最佳。
[Abstract]:Content-based video automatic classification is an important research topic in the field of multimedia analysis, which provides convenience for the increasing management of video data. As a kind of key technology of video propagation control, content-based video automatic classification is very important under the demand of orderly management of network media. Based on the application of automatic video classification technology, media websites can automatically classify the mass of video content, and realize the automatic preliminary screening of bad video information. And it also plays an important role in the development of VOD and intelligent HDTV. The performance of content-based video classification greatly depends on the extraction of video features and the selection of classification models. This paper presents a video feature extraction method based on visual multi-feature combination and an improved support vector machine (SVM) video classification algorithm, which realizes the automatic classification of cartoon, advertisement, music, news and sports. First of all, based on the analysis of the existing video classification algorithms, aiming at the problems existing in the existing algorithms, by analyzing the visual differences of five kinds of typical video, this paper proposes a new feature representation scheme, namely, multi-feature combination model. Fifteen features are extracted from color, texture and motion to form a new visual feature combination model. The selected feature space enhances the compactness of similar sample distribution and the difference of heterogeneous sample distribution. Satisfactory results have been achieved in terms of effectiveness and differentiation. After selecting and extracting suitable features, aiming at the problem that it is difficult to design effective classifier by small sample set in current statistical methods, this paper proposes an automatic video content classification algorithm based on support vector machine (SVM). A one-to-many decision method based on dynamic threshold boundary vector extraction method and a one-to-one decision method based on quadratic prediction mechanism are proposed. And the decision making method of directed acyclic graph based on cross-validation probabilistic error test mechanism. The simulation results show that the algorithm enhances the ability of distinguishing five kinds of video in feature selection, and reduces the computational complexity of single feature. Secondly, it improves the performance of multi-video classification of SVM classifier. The experimental results show that the proposed algorithm has the best performance in classification accuracy.
【学位授予单位】：上海交通大学
【学位级别】：硕士
【学位授予年份】：2009
【分类号】：TP391.41
本文编号：2293871
[Abstract]:Content-based video automatic classification is an important research topic in the field of multimedia analysis, which provides convenience for the increasing management of video data. As a kind of key technology of video propagation control, content-based video automatic classification is very important under the demand of orderly management of network media. Based on the application of automatic video classification technology, media websites can automatically classify the mass of video content, and realize the automatic preliminary screening of bad video information. And it also plays an important role in the development of VOD and intelligent HDTV. The performance of content-based video classification greatly depends on the extraction of video features and the selection of classification models. This paper presents a video feature extraction method based on visual multi-feature combination and an improved support vector machine (SVM) video classification algorithm, which realizes the automatic classification of cartoon, advertisement, music, news and sports. First of all, based on the analysis of the existing video classification algorithms, aiming at the problems existing in the existing algorithms, by analyzing the visual differences of five kinds of typical video, this paper proposes a new feature representation scheme, namely, multi-feature combination model. Fifteen features are extracted from color, texture and motion to form a new visual feature combination model. The selected feature space enhances the compactness of similar sample distribution and the difference of heterogeneous sample distribution. Satisfactory results have been achieved in terms of effectiveness and differentiation. After selecting and extracting suitable features, aiming at the problem that it is difficult to design effective classifier by small sample set in current statistical methods, this paper proposes an automatic video content classification algorithm based on support vector machine (SVM). A one-to-many decision method based on dynamic threshold boundary vector extraction method and a one-to-one decision method based on quadratic prediction mechanism are proposed. And the decision making method of directed acyclic graph based on cross-validation probabilistic error test mechanism. The simulation results show that the algorithm enhances the ability of distinguishing five kinds of video in feature selection, and reduces the computational complexity of single feature. Secondly, it improves the performance of multi-video classification of SVM classifier. The experimental results show that the proposed algorithm has the best performance in classification accuracy.
【学位授予单位】：上海交通大学
【学位级别】：硕士
【学位授予年份】：2009
【分类号】：TP391.41
【引证文献】
相关硕士学位论文 前2条
1 莫咏柳;基于支持向量机的联机手写汉字识别的研究[D];太原理工大学;2011年
2 赵宇熙;一种小型AUV的控制系统研究[D];哈尔滨工程大学;2012年
，本文编号：2293871
本文链接：https://www.wllwen.com/wenyilunwen/guanggaoshejilunwen/2293871.html
