基于局部敏感和可鉴别的稀疏表示的视频镜头分类技术研究.docx
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基于局部敏感和可鉴别的稀疏表示的视频镜头分类技术研究,1.2万字自己原创的毕业论文，已经通过校内系统检测，重复率低，仅在本站独家出售，大家放心下载使用摘要 随着多媒体技术的迅速发展，多媒体数据，尤其是视频数据，正以指数数量级增加。因此，如何快速高效地从海量的视频中检索出所需要的视频显得十分重要，一般都是在基于视频分类的基...
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基于局部敏感和可鉴别的稀疏表示的视频镜头分类技术研究
1.2万字
自己原创的毕业论文，已经通过校内系统检测，重复率低，仅在本站独家出售，大家放心下载使用
摘要 随着多媒体技术的迅速发展，多媒体数据，尤其是视频数据，正以指数数量级增加。因此，如何快速高效地从海量的视频中检索出所需要的视频显得十分重要，一般都是在基于视频分类的基础上查找。传统的视频分类通常是基于标题的，通过人工标注来完成，这种方法效率固然很低。因而基于语义的视频分类应运而生。基于语义视频分类的基础性工作是对视频镜头加以分类，因而研究快速有效的视频镜头分类方法显得尤为重要。
目前的实践已经证明稀疏表示在视频类方面有着优越的性能。但传统稀疏表示方法中，相似视频特征未必能产生相近稀疏表示结果。因此，为了提高视频分类的准确性，本文提出一种基于局部敏感和可鉴别的稀疏表示视频镜头分类算法，引入基于欧式距离的鉴别损失函数，优化构建稀疏表示的字典，以进一步提高视频镜头分类准确率。本文的具体内容排如下：
(1)介绍了基于人工免疫的有序样本聚类算法，在此基础上研究了基于人工免疫有序聚类的视频关键帧提取。
(2)介绍了加权分块颜色直方图、局部二值模式、灰度共生矩阵以及径向Tchebichef矩的特点。在此基础上本文采用了多特征融合的视频特征提取方法。
(3)提出了基于局部敏感和可鉴别的稀疏表示的视频镜头分类算法，通过对稀疏表示的条件加以约束，优化构建稀疏表示字典。实验证明该算法有利于提高视频镜头的分类准确率。
(4)设计并开发了视频镜头分类原型系统。采用面向对象的设计思想，设计实现了基于局部敏感和可鉴别稀疏表示的视频镜头分类原型系统，并验证上述方法的有效性。
关键词：视频镜头分类，可鉴别，稀疏表示，局部敏感
Rearch on video shot classification based on local sensitive and identified sparse representation
Abstract With the rapid development of multimedia technology, particularly video data, is rapidly increasing.How to search the video we need is being more and more important.In general,the way we usually search the video is based on classification. Traditional video classification is usually based on the title, which is completed by manual annotation, and This method is inefficient.thus the aproach based on semantic video classification came into being.The foundation work of classification based on semantic video classification is to classify the video shot,so how to Improve the classification efficiency is being more and more important.
The current practice has proved sparse representation has a superior performance in terms of video category. However, the traditional sparse representation,the similar video feature may not be able to produce similar results based sparse representation. Therefore, in order to improve the accuracy of classification of video, This paper presents a classification algorithm based on said video footage and sensitive identification of local sparse, the differential loss function of the Euclidean distance is introducted into the local sensitive sparse representation algorithm to Optimal Structuring dictionary sparse representation and To further improve the classification accuracy of video camera.
The specific content of this assay is as follow:
(1)Introduces artificial immune clustering algorithm based on ordered samples, based on this study and orderly frame extraction based on artificial immune clustering video key.
(2)Introduced the weighted block color histogram, local binary pattern, GLCM and radial Tchebichef moments characteristics. On the basis of this paper, a video feature extraction method for multi-feature fusion.
(3)Video shot classification algorithm is proposed based on local sensitive and can identify the sparse representation, to be bound by the conditions of the sparse representation, optimizing build sparse representation dictionary. Experimental results show that the algorithm will help improve the classification accuracy of video footage.
(4)Designed and developed a prototype video shot classification system. Object-oriented design, design and implementation of the effectiveness of the method is sensitive and can be identified based on local sparse representation of a video shot classification prototype system, and verified.
Keywords：video classification, identification, local sensitive, sparse representation
目录
第一章绪论 1
1.1课题研究背景 1
1.2国内外发展现状 2
1.3论文内容的安排 3
第二章视频镜头预处理 4
2.1 概述 4
2.2关键帧提取 4
2.3特征值提取 5
2.3.1颜色特征值 5
2.3.2LBP特征提取 7
2.3.3灰度共生矩阵特征提取 7
2.3.4径向Tchebichef矩特征提取 9
2.4多特征融合 9
2.5本章小结 10
第三章局部敏感和可鉴别的稀疏表示 11
3.1 概述 11
3.2基于局部敏感和可鉴别的稀疏表示 11
3.2.1鉴别损失函数的引入 11
3.2.2基于局部敏感和可鉴别稀疏表示字典学习 11
3.3基于局部敏感的可鉴别的稀疏表示分类. 13
3.4TRECVID视频数据集实验结果分析 14
3.4.1训练字典大小的比较 14
3.4.2两种语义概念算法识别率比较 14
3.5本章小结 15
第四章 视频事件特征提取与选择原型系统实现 15
4.1概述 15
4.2开发环境简介 15
4.3系统的功能分析 16
4.4系统核心类库 17
4.4.1 视频关键帧提取 17
4.4.2视频特征提取类的实现 17
4.4.3 分类部分实现 18
4.4.4视频..
1.2万字
自己原创的毕业论文，已经通过校内系统检测，重复率低，仅在本站独家出售，大家放心下载使用
摘要 随着多媒体技术的迅速发展，多媒体数据，尤其是视频数据，正以指数数量级增加。因此，如何快速高效地从海量的视频中检索出所需要的视频显得十分重要，一般都是在基于视频分类的基础上查找。传统的视频分类通常是基于标题的，通过人工标注来完成，这种方法效率固然很低。因而基于语义的视频分类应运而生。基于语义视频分类的基础性工作是对视频镜头加以分类，因而研究快速有效的视频镜头分类方法显得尤为重要。
目前的实践已经证明稀疏表示在视频类方面有着优越的性能。但传统稀疏表示方法中，相似视频特征未必能产生相近稀疏表示结果。因此，为了提高视频分类的准确性，本文提出一种基于局部敏感和可鉴别的稀疏表示视频镜头分类算法，引入基于欧式距离的鉴别损失函数，优化构建稀疏表示的字典，以进一步提高视频镜头分类准确率。本文的具体内容排如下：
(1)介绍了基于人工免疫的有序样本聚类算法，在此基础上研究了基于人工免疫有序聚类的视频关键帧提取。
(2)介绍了加权分块颜色直方图、局部二值模式、灰度共生矩阵以及径向Tchebichef矩的特点。在此基础上本文采用了多特征融合的视频特征提取方法。
(3)提出了基于局部敏感和可鉴别的稀疏表示的视频镜头分类算法，通过对稀疏表示的条件加以约束，优化构建稀疏表示字典。实验证明该算法有利于提高视频镜头的分类准确率。
(4)设计并开发了视频镜头分类原型系统。采用面向对象的设计思想，设计实现了基于局部敏感和可鉴别稀疏表示的视频镜头分类原型系统，并验证上述方法的有效性。
关键词：视频镜头分类，可鉴别，稀疏表示，局部敏感
Rearch on video shot classification based on local sensitive and identified sparse representation
Abstract With the rapid development of multimedia technology, particularly video data, is rapidly increasing.How to search the video we need is being more and more important.In general,the way we usually search the video is based on classification. Traditional video classification is usually based on the title, which is completed by manual annotation, and This method is inefficient.thus the aproach based on semantic video classification came into being.The foundation work of classification based on semantic video classification is to classify the video shot,so how to Improve the classification efficiency is being more and more important.
The current practice has proved sparse representation has a superior performance in terms of video category. However, the traditional sparse representation,the similar video feature may not be able to produce similar results based sparse representation. Therefore, in order to improve the accuracy of classification of video, This paper presents a classification algorithm based on said video footage and sensitive identification of local sparse, the differential loss function of the Euclidean distance is introducted into the local sensitive sparse representation algorithm to Optimal Structuring dictionary sparse representation and To further improve the classification accuracy of video camera.
The specific content of this assay is as follow:
(1)Introduces artificial immune clustering algorithm based on ordered samples, based on this study and orderly frame extraction based on artificial immune clustering video key.
(2)Introduced the weighted block color histogram, local binary pattern, GLCM and radial Tchebichef moments characteristics. On the basis of this paper, a video feature extraction method for multi-feature fusion.
(3)Video shot classification algorithm is proposed based on local sensitive and can identify the sparse representation, to be bound by the conditions of the sparse representation, optimizing build sparse representation dictionary. Experimental results show that the algorithm will help improve the classification accuracy of video footage.
(4)Designed and developed a prototype video shot classification system. Object-oriented design, design and implementation of the effectiveness of the method is sensitive and can be identified based on local sparse representation of a video shot classification prototype system, and verified.
Keywords：video classification, identification, local sensitive, sparse representation
目录
第一章绪论 1
1.1课题研究背景 1
1.2国内外发展现状 2
1.3论文内容的安排 3
第二章视频镜头预处理 4
2.1 概述 4
2.2关键帧提取 4
2.3特征值提取 5
2.3.1颜色特征值 5
2.3.2LBP特征提取 7
2.3.3灰度共生矩阵特征提取 7
2.3.4径向Tchebichef矩特征提取 9
2.4多特征融合 9
2.5本章小结 10
第三章局部敏感和可鉴别的稀疏表示 11
3.1 概述 11
3.2基于局部敏感和可鉴别的稀疏表示 11
3.2.1鉴别损失函数的引入 11
3.2.2基于局部敏感和可鉴别稀疏表示字典学习 11
3.3基于局部敏感的可鉴别的稀疏表示分类. 13
3.4TRECVID视频数据集实验结果分析 14
3.4.1训练字典大小的比较 14
3.4.2两种语义概念算法识别率比较 14
3.5本章小结 15
第四章 视频事件特征提取与选择原型系统实现 15
4.1概述 15
4.2开发环境简介 15
4.3系统的功能分析 16
4.4系统核心类库 17
4.4.1 视频关键帧提取 17
4.4.2视频特征提取类的实现 17
4.4.3 分类部分实现 18
4.4.4视频..
