Modeling and comparing change using spatiotemporal helixes

ABSTRACT

Spatiotemporal helixes are a novel way to model spatiotemporal change. They represent both the movement of an object, as it is expressed by the trajectory of its center, and the changes of its outline. Accordingly they are highly suitable to communicate the evolution of phenomena as they are captured e.g. in sequences of imagery. In this paper we present the spatiotemporal helix model and introduce spatiotemporal similarity metrics for the comparison of helixes. These metrics allow us to compare the behavior of different objects over time, and express the degree of their similarity. To demonstrate the application of our models and metrics we present experimental results.

REFERENCES

• 1: Peggy Agouris , Anthony Stefanidis, Efficient summarization of spatiotemporal events, Communications of the ACM, v.46 n.1, January 2003 [doi>10.1145/602421.602454]
• 2: Agouris P., A. Stefanidis & S. Gyftakis, 2001. Differential Snakes for Change Detection in Road Segments, Photo-grammetric Eng. & Remote Sensing, 67(12): 1391--1399.
• 3: Michael G. Christel , Michael A. Smith , C. Roy Taylor , David B. Winkler, Evolving video skims into useful multimedia abstractions, Proceedings of the SIGCHI conference on Human factors in computing systems, p.171-178, April 18-23, 1998, Los Angeles, California, United States [doi>10.1145/274644.274670]
• 4: Doucette P., P. Agouris, A. Stefanidis, and M. Musavi, 2001. Self-Organized Clustering for Road Extraction in Classified Imagery, ISPRS Journal of Photogrammetry and Remote Sensing, 55(5-6): 347--358.
• 5: Hornsby, K. & M. Egenhofer, 2000. Identity-Based Change: A Foundation for Spatio-temporal Knowledge Representation, Int. Journal of Geographical Information Science 14 (3): 207--224.
• 6: Kass M., A. Witkin, & D. Terzopoulos, 1987. Snakes: Active Contour Models, in Proceedings of the 1st Int. Conf. on Computer Vision, London: 259--268.
• 7: Kohonen, T., 1982. Self-organized Formation of Topolo-gically Correct Feature Maps. Biol. Cybernetics: 59--69.
• 8: Tony Lindeberg, Scale-Space Theory in Computer Vision, Kluwer Academic Publishers, Norwell, MA, 1994
• 9: Liu T-L. & D. Geiger, 1999. Approximate Tree Matching and Shape Similarity. Proc. of 7th IEEE Intl. Conf. on Computer Vision (ICCV'99), Corfu, Greece.
• 10: Oh, J. and K. A. Hua. 2000. An Efficient Technique for Summarizing Videos using Visual Contents. Proc. IEEE Int. Conf. on Multimedia, New York: 1167--1170.
• 11: Dimitris Papadias , Timos Sellis , Yannis Theodoridis , Max J. Egenhofer, Topological relations in the world of minimum bounding rectangles: a study with R-trees, ACM SIGMOD Record, v.24 n.2, p.92-103, May 1995
• 12: Partsinevelos P., A. Stefanidis & P. Agouris, 2001. Automated Spatiotemporal Scaling for Video Generalization, IEEE-ICIP01 (Int. Conf. on Image Processing), Thessaloniki, 1: 177--180.
• 13: Dieter Pfoser , Christian S. Jensen , Yannis Theodoridis, Novel Approaches in Query Processing for Moving Object Trajectories, Proceedings of the 26th International Conference on Very Large Data Bases, p.395-406, September 10-14, 2000
• 14: Pfoser D. & Y. Theodoridis, 2000. Generating Semantics-Based Trajectories of Moving Objects, Int. Workshop on Emerging Techn. for GeoBased Applications, Ascona.
• 15: N. Pissinou , I. Radev , K. Makki , W. J. Campbell, Spatio-Temporal Composition of Video Objects: Representation and Querying in Video Database Systems, IEEE Transactions on Knowledge and Data Engineering, v.13 n.6, p.1033-1040, November 2001 [doi>10.1109/69.971195]
• 16: G. Proietti , C. Faloutsos, Accurate Modeling of Region Data, IEEE Transactions on Knowledge and Data Engineering, v.13 n.6, p.874-883, November 2001 [doi>10.1109/69.971184]
• 17: Pope, A., R. Kumar, H. Sawhney and C. Wan, 1998. Video Abstraction: Summarizing Video Content for Retrieval and Visualization. Proc. 32nd Asilomar Conference of Signals, Systems & Computers: 915--919.
• 18: Yong Rui , Thomas S. Huang , Sharad Mehrotra, Exploring Video Structure Beyond The Shots, Proceedings of the IEEE International Conference on Multimedia Computing and Systems, p.237, June 28-July 01, 1998 [doi>10.1109/MMCS.1998.693648]
• 19: Hanan Samet, The design and analysis of spatial data structures, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1990
• 20: Timos K. Sellis , Nick Roussopoulos , Christos Faloutsos, The R+-Tree: A Dynamic Index for Multi-Dimensional Objects, Proceedings of the 13th International Conference on Very Large Data Bases, p.507-518, September 01-04, 1987
• 21: A. Prasad Sistla , Ouri Wolfson , Sam Chamberlain , Son Dao, Modeling and Querying Moving Objects, Proceedings of the Thirteenth International Conference on Data Engineering, p.422-432, April 07-11, 1997
• 22: Smith M. & T. Kanade, 1995. Video Skimming for Quick Browsing based on Audio and Image Characterization, Tech. Report CMU-CS-95-186, Carnegie Mellon Univ.
• 23: Vazirgiannis M., & O. Wolfson, 2001. A Spatiotemporal Query Language for Moving Point Objects, SSTD '01, LA, pp. 20--35.
• 24: Michail Vlachos , Dimitrios Gunopulos , George Kollios, Robust Similarity Measures for Mobile Object Trajectories, Proceedings of the 13th International Workshop on Database and Expert Systems Applications, p.721-728, September 02-06, 2002
• 25: Ouri Wolfson , Prasad Sistla , Bo Xu , Jutai Zhou , Sam Chamberlain, DOMINO: databases fOr MovINg Objects tracking, Proceedings of the 1999 ACM SIGMOD international conference on Management of data, p.547-549, May 31-June 03, 1999, Philadelphia, Pennsylvania, United States

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing

General Terms:
Algorithms

Keywords:
change modeling, geographic information systems, spatiotemporal analysis