Article

Modeling and comparing change using spatiotemporal helixes

Authors:

Anthony Stefanidis,

Kristin Eickhorst,

Panos PartsinevelosAuthors Info & Claims

GIS '03: Proceedings of the 11th ACM international symposium on Advances in geographic information systems

Pages 86 - 93

https://doi.org/10.1145/956676.956688

Published: 07 November 2003 Publication History

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]

Agouris P. & A. Stefanidis, 2003. Efficient Summarization of Spatiotemporal Events, Communications of the ACM, 46(1), pp. 65--66.

Digital Library

[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]

Christel, M. G., M. A. Smith, C. R. Taylor and D. B. Winkler, 1998. Evolving Video Skims into Useful Multimedia Abstractions. In Proceedings of CHI: 171--178.

Digital Library

[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]

Lindeberg T., 1994. Scale-Space Theory in Computer Vision, Kluwer Academic Publ., Boston.

Digital Library

[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]

Papadias D., Y. Theodoridis, T. Sellis and M. Egenhofer, 1995. Topological Relations in the World of Minimum Bounding Rectangles: A Study with R-Trees. SIGMOD '95, Carey & Schneider (eds.), SIGMOD Record 24 (2): 92--103.

Digital Library

[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]

Pfoser, D., C. Jensen and Y. Theodoridis, 2000. Novel Approaches to the Indexing of moving Object Trajectories. VLDB 2000, pp. 395--406.

Digital Library

[14]

Pfoser D. & Y. Theodoridis, 2000. Generating Semantics-Based Trajectories of Moving Objects, Int. Workshop on Emerging Techn. for GeoBased Applications, Ascona.

[15]

Pissinou N., I. Radev & K. Makki, 2001. Spatio-Temporal Composition of Video Objects: Representation and Querying in Video Database Systems, IEEE Trans. on Knowledge and Data Engineering, 13(6), pp. 1033--1040.

Digital Library

[16]

Proietti G. & C. Faloutsos, 2001. Accurate Modeling of Region Data, IEEE TKDE, 13(6), pp. 874--883.

Digital Library

[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]

Rui, Y., T. S. Huang and S. Mehrotra, 1998. Exploring Video Structure Beyond the Shots. Proc. IEEE Intl. Conf. on Multimedia Computing and Systems, Austin: 237--240.

Digital Library

[19]

Samet, H., 1990. The Design and Analysis of Spatial Data Structures. Addison-Wesley Reading, MA.

Digital Library

[20]

Sellis, T., N. Roussopoulos and C. Faloutsos, 1987. The R+-tree: A Dynamic Index for Multi-Dimensional Objects. Proceedings VLDB '87, Brighton: 507--518.

Digital Library

[21]

Sistla A. P., O. Wolfson, S. Chamberlain, & S. Dao, 1997. Modeling and Querying Moving Objects. ICDE: 422--432.

Digital Library

[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.

Digital Library

[24]

Vlachos, M., D. Gunopulos & G. Kollios, 2002. Robust Similarity Measures for Mobile Object Trajectories, 5th DEXA Workshop on Mobility in Databases and Distributed Systems, Aix-en-Provence, France.

Digital Library

[25]

Wolfson O., A.P.Sistla, B. Xu, J. Zhou, & S. Chamberlain, 1999. DOMINO: Databases fOr MovINg Object tracking. SIGMOD Conference, pp. 547--549.

Digital Library

Cited By

Chen XLi CZhang DQian N(2014)Visualization Methods by using graphs for Geodynamics*2014 Third International Workshop on Earth Observation and Remote Sensing Applications (EORSA)10.1109/EORSA.2014.6927884(231-233)Online publication date: Jun-2014
https://doi.org/10.1109/EORSA.2014.6927884
Talukder APanangadan A(2014)Extreme event detection and assimilation from multimedia sourcesMultimedia Tools and Applications10.1007/s11042-012-1088-y70:1(237-261)Online publication date: 1-May-2014
https://dl.acm.org/doi/10.1007/s11042-012-1088-y
Ujang URahman A(2013)Temporal Three-Dimensional Ontology for Geographical Information Science (GIS)—A ReviewJournal of Geographic Information System10.4236/jgis.2013.5303005:03(314-323)Online publication date: 2013
https://doi.org/10.4236/jgis.2013.53030
Show More Cited By

Index Terms

Modeling and comparing change using spatiotemporal helixes
1. Information systems
  1. Information retrieval
    1. Document representation

Recommendations

Modeling and comparing spatiotemporal events
dg.o '04: Proceedings of the 2004 annual national conference on Digital government research

Spatiotemporal helixes constitute a novel method we developed for modeling changes in an object over time. Changes in both an object's trajectory and its outline can be represented with these helixes. In addition, spatiotemporal helixes can be compared ...
Indexing spatiotemporal archives

Spatiotemporal objects – that is, objects that evolve over time – appear in many applications. Due to the nature of such applications, storing the evolution of objects through time in order to answer historical queries (queries that refer to past states ...
Spatiotemporal Data Modeling and Management: A Survey
TOOLS '00: Proceedings of the 36th International Conference on Technology of Object-Oriented Languages and Systems (TOOLS-Asia'00)

Many data objects in the real world have attributes about location and time. Such spatiotemporal objects can be found in applications such as Geographic Information Systems (GIS), environmental data management and multimedia databases. Traditional ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

GIS '03: Proceedings of the 11th ACM international symposium on Advances in geographic information systems

November 2003

180 pages

ISBN:1581137303

DOI:10.1145/956676

Program Chairs:
Erik Hoel
ESRI, USA
,
Philippe Rigaux
LRI, University Paris-Sud Orsay, France

Copyright © 2003 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2003

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

CIKM03

Sponsor:

CIKM03: 12th International Conference on Information and Knowledge Management

November 7 - 8, 2003

Louisiana, New Orleans, USA

Acceptance Rates

Overall Acceptance Rate 220 of 1,116 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
500
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 22 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Chen XLi CZhang DQian N(2014)Visualization Methods by using graphs for Geodynamics*2014 Third International Workshop on Earth Observation and Remote Sensing Applications (EORSA)10.1109/EORSA.2014.6927884(231-233)Online publication date: Jun-2014
https://doi.org/10.1109/EORSA.2014.6927884
Talukder APanangadan A(2014)Extreme event detection and assimilation from multimedia sourcesMultimedia Tools and Applications10.1007/s11042-012-1088-y70:1(237-261)Online publication date: 1-May-2014
https://dl.acm.org/doi/10.1007/s11042-012-1088-y
Ujang URahman A(2013)Temporal Three-Dimensional Ontology for Geographical Information Science (GIS)—A ReviewJournal of Geographic Information System10.4236/jgis.2013.5303005:03(314-323)Online publication date: 2013
https://doi.org/10.4236/jgis.2013.53030
Yuan M(2010)Geographic Data StructuresManual of Geospatial Science and Technology, Second Edition10.1201/9781420087345-c28(549-573)Online publication date: 28-Jun-2010
https://doi.org/10.1201/9781420087345-c28
Pultar ECova TYuan MGoodchild M(2010)EDGIS: a dynamic GIS based on space time pointsInternational Journal of Geographical Information Science10.1080/1365881080264456724:3(329-346)Online publication date: 10-Mar-2010
https://doi.org/10.1080/13658810802644567
Stewart Hornsby KKing K(2008)Modeling Motion Relations for Moving Objects on Road NetworksGeoinformatica10.1007/s10707-007-0039-712:4(477-495)Online publication date: 1-Dec-2008
https://dl.acm.org/doi/10.1007/s10707-007-0039-7
Agouris PGunopulos DKalogeraki VStefanidis A(2008)Knowledge Aquisition and Data Storage in Mobile GeoSensor NetworksGeoSensor Networks10.1007/978-3-540-79996-2_6(86-108)Online publication date: 1-Mar-2008
https://dl.acm.org/doi/10.1007/978-3-540-79996-2_6
Stewart Hornsby KKing K(2008)Linking Geosensor Network Data and Ontologies to Support Transportation ModelingGeoSensor Networks10.1007/978-3-540-79996-2_11(191-209)Online publication date: 1-Mar-2008
https://dl.acm.org/doi/10.1007/978-3-540-79996-2_11
Hornsby KCole S(2007)Modeling Moving Geospatial Objects from an Event‐based PerspectiveTransactions in GIS10.1111/j.1467-9671.2007.01060.x11:4(555-573)Online publication date: 27-Jul-2007
https://doi.org/10.1111/j.1467-9671.2007.01060.x
Goodchild MYuan MCova T(2007)Towards a general theory of geographic representation in GISInternational Journal of Geographical Information Science10.1080/1365881060096527121:3(239-260)Online publication date: 1-Jan-2007
https://dl.acm.org/doi/10.1080/13658810600965271
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents