摘要
互联网环境中大规模的视频拷贝检测面临拷贝变化多样性问题和数据量大的问题,需要使用鲁棒、精简的视觉特征.提出以视频连续帧中关键点的轨迹行为作为内容匹配的特征.关键点轨迹的运动行为不受拷贝变化的影响,利用其特征可以实现鲁棒性匹配.采用马尔可夫链模型建模轨迹的行为过程,将每条轨迹表示为一个25维的精简向量特征.使用时序一致性匹配方法定位视频拷贝的段落.在标准数据集上的对比实验证明:提出的算法在各种常见的拷贝变化下可以得到较高的检测精度,特征匹配的时空消耗低,对大规模的视频拷贝检测行之有效.
Large scale video copy detection is to detect copied segments of provided video content from large video databases.This application requires compact feature which is insensitive to various visual copy changes.However,traditional image features are prone to spatial changes such as color and texture transformations.The reason is the copied versions have large image transformations including global quality decrease and local visual distortions,which dramatically change the distribution of visual features.Consequently,previous methods based on histogram features and ordinal measures failed in copy detection.To solve this problem,this paper proposes the use of invariant visual features based on keypoint trajectory behavior.Instead of using the spatial cues,the proposed approach models the temporal information as robust features.Temporal cues are quantified based on keypoint trajectories which are insensitive to strong changes.Then videos are represented by spatio-temporal features which are more robust to copy changes.Bag of trajectory（BoT） technigue is adopted for fast pattern matching in large database.The experimental results show that spatio-temporal trajectory features are robust to various visual changes,including image blur,scale and ratio changes,and minor frame rate change.Compared with the state-of-art scheme using ordinal measure,the proposed algorithm with lower cost presents better accuracy.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2010年第11期1871-1877,共7页
Journal of Computer Research and Development
基金
国家"九七三"重点基础研究发展计划基金项目(2007CB311100)
国家"八六三"高技术研究发展计划基目项目(2007AA01Z416)
国家自然科学基金项目(60873165
60802028)
北京市科技新星计划基金项目(2007B071)
北京市教育委员会共建项目~~
关键词
视频拷贝检测
时空视觉轨迹
局部关键点检测
时序一致性匹配
马尔可夫链模型
video copy detection
spatio-temporal trajectory
local feature point detection
matching based on temporal information
Markov stationary feature
