Similarity of Cardinal Directions
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.
KeywordsTarget Object Reference Object Cardinal Direction Similarity Assessment Basic Feasible Solution
Unable to display preview. Download preview PDF.
- 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
- 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
- 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
- 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