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Similarity of Cardinal Directions

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Advances in Spatial and Temporal Databases (SSTD 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2121))

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Abstract

Like people who casually assess similarity between spatial scenes in their routine activities, users of pictorial databases are often interested in retrieving scenes that are similar to a given scene, and ranking them according to degrees of their match. For example, a town architect would like to query a database for the towns that have a landscape similar to the landscape of the site of a planned town. In this paper, we develop a computational model to determine the directional similarity between extended spatial objects, which forms a foundation for meaningful spatial similarity operators. The model is based on the direction-relation matrix. We derive how the similarity assessment of two direction-relation matrices corresponds to determining the least cost for transforming one direction-relation matrix into another. Using the transportation algorithm, the cost can be determined efficiently for pairs of arbitrary direction-relation matrices. The similarity values are evaluated empirically with several types of movements that create increasingly less similar direction relations. The tests confirm the cognitive plausibility of the similarity model.

This work was partially supported by the National Imagery and Mapping Agency under grant number NMA202-97-1-1023. Max Egenhofer’s research is further supported by the National Science Foundation under grant numbers IRI-9613646, US-9970123, and EPS-9983432; by the National Imagery and Mapping Agency under grant NMA201-00-1-2009; by the National Institute of Environmental Health Sciences, NIH, under grant number 1 R 01 ES09816-01, and by a contract with Lockheed Martin M&DS.

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References

  • W. Al-Khatib, Y. Day, A. Ghafoor, and P. Berra (1999) Semantic Modeling and Knowledge Representation in Multimedia Databases. IEEE Transactions on Knowledge and Data Engineering 11(1): 64–80.

    Article  Google Scholar 

  • Y. Aslandogan and C. Yu (1999) Techniques and Systems for Image and Video Retrieval. IEEE Transactions on Knowledge and Data Engineering 11(1): 56–63.

    Article  Google Scholar 

  • T. Bruns and M. Egenhofer (1996) Similarity of Spatial Scenes. in: M.-J. Kraak and M. Molenaar (eds.), Seventh International Symposium on Spatial Data Handling, Delft, The Netherlands, pp. 173–184.

    Google Scholar 

  • W. Chu, C. Hsu, A. Cardenas, and R. Taira (1998) Knowledge-based Image Retrieval with Spatial and Temporal Constructs. IEEE Transactions on Knowledge and Data Engineering 10(6): 872–888.

    Article  Google Scholar 

  • W. Chu, I. Leong, and R. Taira (1994) A Semantic Modeling Approach for Image Retrieval by Content. VLDB Journal 3(4): 445–477.

    Article  Google Scholar 

  • G. Dantzig (1963) Linear Programming And Extensions. Princeton University Press, Princeton, NJ.

    MATH  Google Scholar 

  • G. Dantzig and M. Thapa (1997) Linear Programming. Springer-Verlag, New York.

    MATH  Google Scholar 

  • A. DelBimbo and P. Pala (1997) Visual Image Retrieval by Elastic Matching of User Sketches. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(2): 121–132.

    Article  Google Scholar 

  • A. DelBimbo, E. Vicario, and D. Zingoni (1995) Symbolic Description and Visual Querying of Image Sequences Using Spatio-Temporal Logic. IEEE Transactions on Knowledge and Data Engineering 7(4): 609–622.

    Article  Google Scholar 

  • M. Egenhofer (1997) Query Processing in Spatial-Query-by-Sketch. Journal of Visual Languages and Computing 8(4): 403–424.

    Article  Google Scholar 

  • M. Egenhofer and K. Al-Taha (1992) Reasoning About Gradual Changes of Topological Relationships. in: A. Frank, I. Campari, and U. Formentini (eds.), Theories and Methods of Spatio-Temporal Reasoning in Geographic Space, Pisa, Italy, Lecture Notes in Computer Science, 639: 196–219.

    Google Scholar 

  • M. Flickner, H. Sawhney, W. Niblack, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker (1995) Query by Image and Video Content: The QBIC System. IEEE Computer 28(9): 23–32.

    Google Scholar 

  • C. Freksa (1992) Temporal Reasoning Based on Semi-Intervals. Artificial Intelligence 54: 199–227.

    Article  MathSciNet  Google Scholar 

  • R. Goyal (2000) Similarity Assessment for Cardinal Directions Between Extended Spatial Objects. Ph.D. Thesis, Department of Spatial Information Science and Engineering, University of Maine, Orono, ME, http://www.spatial.maine.edu/Publications/phd_thesis/Goyal2000.pdf

    Google Scholar 

  • R. Goyal and M. Egenhofer (in press) Cardinal Directions between Extended Spatial Objects. IEEE Transactions in Knowledge and Data Engineering (in press).

    Google Scholar 

  • R. Goyal and M. Egenhofer (2000) Consistent Queries over Cardinal Directions across Different Levels of Detail. in: A.M. Tjoa, R. Wagner, and A. Al-Zobaidie (eds.), 11th International Workshop on Database and Expert Systems Applications, Greenwich, U.K. pp. 876–880.

    Google Scholar 

  • R. Gonzalez and R. Woods (1992) Digital Image Processing. Addison-Wesley Publishing Company, Reading, MA.

    Google Scholar 

  • V. Gudivada and V. Raghavan (1995) Design and Evaluation of Algorithms for Image Retrieval by Spatial Similarity. ACM Transactions on Information Systems 13(2): 115–144.

    Article  Google Scholar 

  • H. Jiang and A. Elmagarmid (1998) Spatial and Temporal Content-based Access to Hypervideo Databases. VLDB Journal 7: 226–238.

    Article  Google Scholar 

  • K. Murty (1976) Linear and Combinatorial Programming. John Wiley & Sons, Inc., New York.

    MATH  Google Scholar 

  • M. Nabil, J. Shepherd, and A. Ngu (1995) 2D Projection Interval Relationships: A Symbolic Representation of Spatial Relationships. in: M. Egenhofer and J. Herring (eds.), Advances in Spatial Databases—4th International Symposium, SSD’ 95, Portland, ME, Lecture Notes in Computer Science, 951: 292–309 Springer-Verlag, Berlin.

    Google Scholar 

  • D. Papadias and V. Delis (1997) Relation-Based Similarity. Fifth ACM Workshop on Advances in Geographic Information Systems, Las Vegas, NV, pp. 1–4.

    Google Scholar 

  • N. Pissinou, I. Radev, K. Makki, and W. Campbell (1998) A Topological-Directional Model for the Spatio-Temporal Composition of the Video Objects. in: A. Silberschatz, A. Zhang and S. Mehrotra (eds.), Eighth International Workshop on Research Issues on Data Engineering, Continuous-Media Databases and Applications, Orlando, FL, pp. 17–24.

    Google Scholar 

  • A. P. Sistla, C. Yu, C. Liu, and K. Liu (1995) Similarity based Retrieval of Pictures Using Indices on Spatial Relationships, in: U. Dayal, P. Gray, and S. Nishio (eds.), 21st International Conference on Very Large Data Bases, Zurich,. Switzerland, pp. 619–629.

    Google Scholar 

  • J. Strayer (1989) Linear Programming and Its Applications. Springer-Verlag, New York.

    MATH  Google Scholar 

  • A. Tversky (1977) Features of Similarity. Psychological Review 84(4): 327–352.

    Article  Google Scholar 

  • A. Yoshitaka and T. Ichikawa (1999) A Survey on Content-based Retrieval for Multimedia Databases. IEEE Transactions on Knowledge and Data Engineering 11(1): 81–93.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. ESRI, 380 New York Street, Redlands, CA, 92373-8100, USA

    Roop K. Goyal

  2. National Center for Geographic Information and Analysis Department of Spatial Information Science and Engineering Department of Computer Science Boardman Hall, University of Maine, Orono, ME, 04469-5711, USA

    Max J. Egenhofer

Authors
  1. Roop K. Goyal
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  2. Max J. Egenhofer
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Aalborg University, Fredrik Bajers Vej 7E, 9220, Aalborg Øst, Denmark

    Christian S. Jensen

  2. Praktische Informatik IV, FernUniversität Hagen, 58084, Hagen, Germany

    Markus Schneider

  3. Fachbereich Mathematik und Informatik, Philipps-Universität Marburg, 35032, Marburg, Germany

    Bernhard Seeger

  4. Department of Computer Science and Engineering, University of California, Riverside, CA, 92521, USA

    Vassilis J. Tsotras

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© 2001 Springer-Verlag Berlin Heidelberg

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Goyal, R.K., Egenhofer, M.J. (2001). Similarity of Cardinal Directions. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds) Advances in Spatial and Temporal Databases. SSTD 2001. Lecture Notes in Computer Science, vol 2121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47724-1_3

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  • DOI: https://doi.org/10.1007/3-540-47724-1_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42301-0

  • Online ISBN: 978-3-540-47724-2

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