Introduction to algorithms
On mixture density and maximum likelihood power estimation via expectation-maximization
ASP-DAC '00 Proceedings of the 2000 Asia and South Pacific Design Automation Conference
VIDEX: an integrated generic video indexing approach
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
ACM Computing Surveys (CSUR)
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Alternatives to the k-means algorithm that find better clusterings
Proceedings of the eleventh international conference on Information and knowledge management
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Robust Similarity Measures for Mobile Object Trajectories
DEXA '02 Proceedings of the 13th International Workshop on Database and Expert Systems Applications
Subgraph Isomorphism Detection in Polynominal Time on Preprocessed Model Graphs
ACCV '95 Invited Session Papers from the Second Asian Conference on Computer Vision: Recent Developments in Computer Vision
Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Symbolic representation and retrieval of moving object trajectories
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Tracking of moving objects based on graph edges similarity
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
On the marriage of Lp-norms and edit distance
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Spatio-Temporal Indexing for Large Multimedia Applications
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
Automated high-level movie segmentation for advanced video-retrieval systems
IEEE Transactions on Circuits and Systems for Video Technology
Analysis of vector space model and spatiotemporal segmentation for video indexing and retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Content based video matching using spatiotemporal volumes
Computer Vision and Image Understanding
Graphs-at-a-time: query language and access methods for graph databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
On efficiently searching trajectories and archival data for historical similarities
Proceedings of the VLDB Endowment
Bounded coordinate system indexing for real-time video clip search
ACM Transactions on Information Systems (TOIS)
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Robust Adaptable Video Copy Detection
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Ontology-supported video modeling and retrieval
AMR'06 Proceedings of the 4th international conference on Adaptive multimedia retrieval: user, context, and feedback
New application of graph mining to video analysis
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Speeding up complex video copy detection queries
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
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In this paper, we propose new graph-based data structure and indexing to organize and retrieve video data. Several researches have shown that a graph can be a better candidate for modeling semantically rich and complicated multimedia data. However, there are few methods that consider the temporal feature of video data, which is a distinguishable and representative characteristic when compared with other multimedia (i.e., images). In order to consider the temporal feature effectively and efficiently, we propose a new graph-based data structure called Spatio-Temporal Region Graph (STRG). Unlike existing graph-based data structures which provide only spatial features, the proposed STRG further provides temporal features, which represent temporal relationships among spatial objects. The STRG is decomposed into its subgraphs in which redundant subgraphs are eliminated to reduce the index size and search time, because the computational complexity of graph matching (subgraph isomorphism) is NP-complete. In addition, a new distance measure, called Extended Graph Edit Distance (EGED), is introduced in both non-metric and metric spaces for matching and indexing respectively. Based on STRG and EGED, we propose a new indexing method STRG-Index, which is faster and more accurate since it uses tree structure and clustering algorithm. We compare the STRG-Index with the M-tree, which is a popular tree-based indexing method for multimedia data. The STRG-Index outperforms the M-tree for various query loads in terms of cost and speed.