Ordinal Measures for Image Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Detection of video sequences using compact signatures
ACM Transactions on Information Systems (TOIS)
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Scalable mining of large video databases using copy detection
MM '08 Proceedings of the 16th ACM international conference on Multimedia
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Video copy detection by fast sequence matching
Proceedings of the ACM International Conference on Image and Video Retrieval
Scalable clip-based near-duplicate video detection with ordinal measure
Proceedings of the ACM International Conference on Image and Video Retrieval
Multiple feature hashing for real-time large scale near-duplicate video retrieval
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
An Image-Based Approach to Video Copy Detection With Spatio-Temporal Post-Filtering
IEEE Transactions on Multimedia
On the Annotation of Web Videos by Efficient Near-Duplicate Search
IEEE Transactions on Multimedia
Frame filtering and path verification for improving video copy detection
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
| Hi-index | 0.00 |
Various methods of content-based video copy detection have been proposed to find video copies in a large video database. In this paper, we represent video feature obtained by global and/or local detectors as signature time series. We observe that the curves of such time series under various kinds of modifications and transformations follow similar trends. Based on this observation, we propose to use linear segmentation to approximate the time series and extract major inclines from those linear segments. We develop a major incline-based fast alignment method to find potential alignment positions between the compared videos. Further, taking advantage of the major incline-based fast alignment, a Frame Insertion, Deletion, and Substitutions (FIDS) detection method is introduced to detect video copies in the presence of frame order changes. Our proposed solution is simple and generic. It can be combined with existing global or local feature descriptions, and with sequence or keyframe based matching schemes. It speeds up the video copy detection process by reducing the search space to the areas suggested by the potential alignments. Experiments using both the MUSCLE VCD 2007 and TRECVID CBCD 2009 datasets show that the proposed solution significantly accelerates the overall video copy detection process and at the same time achieves good precision.