String searching algorithms
Trie methods for text and spatial data on secondary storage
Trie methods for text and spatial data on secondary storage
CVEPS - a compressed video editing and parsing system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Ordinal Measures for Image Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Indexing and retrieval of video based on spatial relation sequences
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
VIDEX: an integrated generic video indexing approach
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Handbook of Image and Video Processing
Handbook of Image and Video Processing
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
A Novel Motion-Based Active Video Indexing Method
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Video Indexing Using MPEG Motion Compensation Vectors
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
On the detection and recognition of television commercials
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
Detection of video sequences using compact signatures
ACM Transactions on Information Systems (TOIS)
Semantic video fingerprinting and retrieval using face information
Image Communication
Proceedings of the ACM International Conference on Image and Video Retrieval
Hi-index | 0.01 |
Similarity matching in video databases is of growing importance in many new applications such as video clustering and digital video libraries. In order to provide efficient access to relevant data in large databases, there have been many research efforts in video indexing with diverse spatial and temporal features. However, most of the previous works relied on sequential matching methods or memory-based inverted file techniques, thus making them unsuitable for a large volume of video databases. In order to resolve this problem, this paper proposes an effective and scalable indexing technique using a trie, originally proposed for string matching, as an index structure. For building an index, we convert each frame into a symbol sequence using a window order heuristic and build a disk-resident trie from a set of symbol sequences. For query processing, we perform a depth-first traversal on the trie and execute a temporal segmentation. To verify the superiority of our approach, we perform several experiments with real and synthetic data sets. The results reveal that our approach consistently outperforms the sequential scan method, and the performance gain is maintained even with a large volume of video databases.