A Graph-Based Approach for Modeling and Indexing Video Data

In this work, we propose new graph-based data model and indexing to organize and manage video data. To consider spatial and temporal characteristics of video, we introduce a new graph-based data model 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 object graphs (OGs) and background graphs (BGs). In addition, a new distance measure, called extended graph edit distance (EGED), is introduced in metric space for matching and indexing. Based on clustering and EGED, we propose a new indexing method STRG-index, which is faster and more accurate. 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 in terms of cost and speed

[1]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[2]  Özgür Ulusoy,et al.  BilVideo Video Database Management System , 2004, VLDB.

[3]  Harald Kosch,et al.  VIDEX: an integrated generic video indexing approach , 2000, MM 2000.

[4]  Vamsi K. Srikantam,et al.  On mixture density and maximum likelihood power estimation via expectation-maximization , 2000, ASP-DAC '00.

[5]  Xufeng Niu,et al.  A space–time model for seasonal hurricane prediction , 2002 .

[6]  Xindong Wu,et al.  Video data mining: semantic indexing and event detection from the association perspective , 2005, IEEE Transactions on Knowledge and Data Engineering.

[7]  Svetha Venkatesh,et al.  Spatial Indexing for Video Databases , 1996, J. Vis. Commun. Image Represent..

[8]  Laura Maruster,et al.  Encyclopedia of data warehousing and mining , 2008 .

[9]  A. Murat Tekalp,et al.  Content-based video abstraction , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[10]  Lei Chen,et al.  Robust and fast similarity search for moving object trajectories , 2005, SIGMOD '05.

[11]  Timos K. Sellis,et al.  Spatio-temporal indexing for large multimedia applications , 1996, Proceedings of the Third IEEE International Conference on Multimedia Computing and Systems.

[12]  Andrew W. Moore,et al.  X-means: Extending K-means with Efficient Estimation of the Number of Clusters , 2000, ICML.

[13]  T. Ebrahimi,et al.  Change detection and background extraction by linear algebra , 2001, Proc. IEEE.

[14]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[15]  Javier Béjar,et al.  Generality-Based Conceptual Clustering with Probabilistic Concepts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Duane Szafron,et al.  Modeling of moving objects in a video database , 1997, Proceedings of IEEE International Conference on Multimedia Computing and Systems.

[17]  Walid G. Aref,et al.  Smart VideoText: a video data model based on conceptual graphs , 2002, Multimedia Systems.

[18]  Christos Faloutsos,et al.  Fast subsequence matching in time-series databases , 1994, SIGMOD '94.

[19]  Mario Vento,et al.  Graph matching applications in pattern recognition and image processing , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[20]  Michael R. Lyu,et al.  Video summarization by spatial-temporal graph optimization , 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).

[21]  Jung-Hwan Oh,et al.  STRG-Index: spatio-temporal region graph indexing for large video databases , 2005, SIGMOD '05.

[22]  Andreas Hotho,et al.  Conceptual Clustering of Text Clusters , 2003 .

[23]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[24]  Michael D. Abràmoff,et al.  Image processing with ImageJ , 2004 .

[25]  Markus Schneider.  Evaluation of Spatio-Temporal Predicates on Moving Objects , 2005, ICDE.

[26]  Steven J. Fenves,et al.  The formation and use of abstract concepts in design , 1991 .

[27]  Adrian E. Raftery,et al.  Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .

[28]  Arif Ghafoor,et al.  Object-oriented conceptual modeling of video data , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[29]  Nicu Sebe,et al.  Similarity Matching in Computer Vision and Multimedia , 2008, Comput. Vis. Image Underst..

[30]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Kevin Thompson,et al.  Cobweb/3: A portable implementation , 1990 .

[32]  Alan Hanjalic,et al.  Automated high-level movie segmentation for advanced video-retrieval systems , 1999, IEEE Trans. Circuits Syst. Video Technol..

[33]  R. G. G. Cattell,et al.  Recent books , 2000, IEEE Spectrum.

[34]  Walid G. Aref,et al.  Video query processing in the VDBMS testbed for video database research , 2003, MMDB '03.

[35]  Gultekin Özsoyoglu,et al.  A graph query language and its query processing , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[36]  G. Levi.  A note on the derivation of maximal common subgraphs of two directed or undirected graphs , 1973 .

[37]  Elke A. Rundensteiner,et al.  MMVIS: design and implementation of a multimedia visual information seeking environment , 1997, MULTIMEDIA '96.

[38]  Amir Averbuch,et al.  Tracking of moving objects based on graph edges similarity , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[39]  Hong Heather Yu,et al.  A visual search system for video and image databases , 1997, Proceedings of IEEE International Conference on Multimedia Computing and Systems.

[40]  David S. Doermann,et al.  Building mosaics from video using MPEG motion vectors , 1999, MULTIMEDIA '99.

[41]  Pavel Zezula,et al.  M-tree: An Efficient Access Method for Similarity Search in Metric Spaces , 1997, VLDB.

[42]  Roger Mohr,et al.  Probabilistic Hierarchical Framework for Clustering of Tracked Objects in Video Streams , 2000 .

[43]  Christos Faloutsos,et al.  Automatic multimedia cross-modal correlation discovery , 2004, KDD.

[44]  Ann Q. Gates,et al.  TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2005 .

[45]  Douglas H. Fisher,et al.  Knowledge Acquisition Via Incremental Conceptual Clustering , 1987, Machine Learning.

[46]  Arbee L. P. Chen,et al.  Content-Based Query Processing for Video Databases , 2000, IEEE Trans. Multim..

[47]  Dimitrios Gunopulos,et al.  Robust similarity measures for mobile object trajectories , 2002, Proceedings. 13th International Workshop on Database and Expert Systems Applications.

[48]  Herbert Gish,et al.  Parametric trajectory models for speech recognition , 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.

[49]  Horst Bunke,et al.  A graph distance metric based on the maximal common subgraph , 1998, Pattern Recognit. Lett..

[50]  Chong-Wah Ngo,et al.  Video summarization and scene detection by graph modeling , 2005, IEEE Transactions on Circuits and Systems for Video Technology.

[51]  Hwann-Tzong Chen,et al.  Multi-object tracking using dynamical graph matching , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[52]  John Adcock,et al.  FXPAL Experiments for TRECVID 2004 , 2004, TRECVID.

[53]  Ge Yu,et al.  M+-tree : A New Dynamical Multidimensional Index for Metric Spaces , 2003, ADC.

[54]  Ricardo A. Baeza-Yates,et al.  Searching in metric spaces , 2001, CSUR.

[55]  Wesley W. Chu,et al.  An error-based conceptual clustering method for providing approximate query answers , 1996, CACM.

[56]  Raymond T. Ng,et al.  Indexing spatio-temporal trajectories with Chebyshev polynomials , 2004, SIGMOD '04.

[57]  Jung-Hwan Oh,et al.  Clustering of Video Objects by Graph Matching , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[58]  M. ZloofM..  Query-by-example , 1977 .

[59]  Charles Elkan,et al.  Using the Triangle Inequality to Accelerate k-Means , 2003, ICML.

[60]  Lei Chen,et al.  On The Marriage of Lp-norms and Edit Distance , 2004, VLDB.

[61]  Beng Chin Ooi,et al.  Indexing the Distance: An Efficient Method to KNN Processing , 2001, VLDB.

[62]  Chinatsu Aone,et al.  Fast and effective text mining using linear-time document clustering , 1999, KDD '99.

[63]  Horst Bunke,et al.  Subgraph Isomorphism Detection in Polynominal Time on Preprocessed Model Graphs , 1995, ACCV.

[64]  Rune Hjelsvold,et al.  Modelling and Querying Video Data , 1994, VLDB.

[65]  Horst Bunke,et al.  A Comparison of Algorithms for Maximum Common Subgraph on Randomly Connected Graphs , 2002, SSPR/SPR.

[66]  Edoardo Ardizzone,et al.  Automatic Video Database Indexing and Retrieval , 2004, Multimedia Tools and Applications.

[67]  Christian Böhm,et al.  Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases , 2001, CSUR.

[68]  J. J. McGregor,et al.  Backtrack search algorithms and the maximal common subgraph problem , 1982, Softw. Pract. Exp..

[69]  Ahmed K. Elmagarmid,et al.  VideoText database systems , 1997, Proceedings of IEEE International Conference on Multimedia Computing and Systems.

[70]  Sergio A. Velastin,et al.  People tracking in surveillance applications , 2006, Image Vis. Comput..

[71]  Horst Bunke,et al.  On a relation between graph edit distance and maximum common subgraph , 1997, Pattern Recognit. Lett..

[72]  Markus Schneider,et al.  Spatio-Temporal Predicates , 2002, IEEE Trans. Knowl. Data Eng..

[73]  B. S. Manjunath,et al.  Unsupervised Segmentation of Color-Texture Regions in Images and Video , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[74]  M. Emre Celebi,et al.  Classification of Bleeding Images in Wireless Capsule Endoscopy using HSI Color Domain and Region Segmentation , 2007 .

[75]  Philip S. Yu,et al.  Motion adaptive indexing for moving continual queries over moving objects , 2004, CIKM '04.