Automatic partitioning of full-motion video
Multimedia Systems
MULTIMEDIA '94 Proceedings of the second ACM international conference on Multimedia
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Retrieval
Automatic Video Indexing and Full-Video Search for Object Appearances
Proceedings of the IFIP TC2/WG 2.6 Second Working Conference on Visual Database Systems II
Video Cut Detection using Frequency Domain Correlation
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Key frame selection by motion analysis
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
A multi-layer video browsing system
IEEE Transactions on Consumer Electronics
Using color chains similarities for MLB sports image retrieval
CSECS '10 Proceedings of the 9th WSEAS international conference on Circuits, systems, electronics, control & signal processing
Hi-index | 0.00 |
This paper describes a method for video retrieval system based on local invariant region descriptors. A novel framework is proposed for combined video segmentation, content extraction and retrieval. A similarity measure, previously proposed by the authors based on local region features, is used for video segmentation. The local regions are tracked throughout a shot and stable features are extracted. The conventional key frame method is replaced with these stable local features to characterise different shots. A grouping technique is introduced to combine these stable tracks into meaningful object clusters. The above method can handle the different scales of object appearance in videos. Compared to previous video retrieval approaches, the proposed method is highly robust to camera and object motions and can withstand severe illumination changes. The proposed framework is applied to scene and object retrieval experiments and significant improvement in performance is demonstrated.