This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Knowledge Structuring and Constraint Satisfaction: The Mapsee Approach
November 1988 (vol. 10 no. 6)
pp. 866-879

Schema-based representations for visual knowledge are integrated with constraint satisfaction techniques. This integration is discussed in a progression of three sketch map interpretation programs: Mapsee-1, Mapsee-2, and Mapsee-3. The programs are evaluated by the criteria of descriptive and procedural adequacy. The evaluation indicates that a schema-based representation used in combination with a hierarchical arc-consistency algorithm constitutes a modular, efficient, and effective approach to the structured representation of visual knowledge. The schemata used in this representation are embedded in composition and specialization hierarchies. Specialization hierarchies are further expanded into discrimination graphs.

[1] D. Ballard, C. Brown, and J. Feldman, "An approach to knowledge-directed image analysis," inComputer Vision Systems, A. Hanson and E. Riseman, Eds. New York: Academic, 1978, pp. 271-282.
[2] H. G. Barrow and J. M. Tenenbaum, "Recovering intrinsic scene characteristics from images," inComputer Vision Systems, A. R. Hanson and E. M. Riseman, Eds. New York: Academic, 1978, pp. 3-26.
[3] F. C. Bartlett,Remembering, A Study in Experimental and Social Psychology. Cambridge: Cambridge University Press, 1932.
[4] T. Binford, "Survey of model-based image analysis systems,"Int. J. Robotics Res., vol. 1, pp. 18-64, 1982.
[5] D. G. Bobrow and T. Winograd, "An overview of KRL, a knowledge representation language,"Cognitive Sci., vol. 1, no. 1, pp. 3-46, 1977.
[6] R. C. Bolles, L. H. Quam, M. A. Fischler, and H. C. Wolf, "Automatic determination of image-to-database correspondences," inProc. Sixth Int. Joint Conf. Artificial Intelligence, Tokyo, 1979, pp. 73-78.
[7] R. A. Brooks, "Symbolic reasoning among 3-D models and 2-D images,"Artificial Intell., vol. 17, no. 1-3, pp. 285-348, 1981.
[8] E. C. Freuder, "Synthesizing constraint expressions,"Comm. ACM, vol. 21, no. 11, pp. 958-966, 1978.
[9] J. Glicksman, "Using multiple information sources in a computational vision system," inProc. Eighth Int. Joint Conf. Artificial Intelligence, Karlsruhe, 1983, pp. 1078-1080.
[10] A. R. Hanson and E. M. Riseman, "Visions: A computer system for interpreting scenes," inComputer Vision Systems, A. R. Hanson and E. M. Riseman, Eds. New York: Academic, 1978, pp. 303-334.
[11] W. S. Havens, "A procedural model of recognition for machine perception," Dep. Comput. Sci., Univ. British Columbia, Vancouver, Tech. Rep. TR-78-3, 1978.
[12] W. S. Havens and A. K. Mackworth, "Structuring domain knowledge for visual perception," inProc. Seventh Int. Joint Conf. Artificial Intelligence, Vancouver, B.C., 1981, pp. 625-627.
[13] W. S. Havens and A. K. Mackworth, "Representing knowledge of the visual world,"Computer, vol. 16, no. 10, pp. 90-98, 1983.
[14] W. S. Havens, "A theory of schema labelling,"Computational Intell., vol. 1, no. 3-4, pp. 127-139, 1985.
[15] R. A. Hummel and S. W. Zucker, "On the foundations of relaxation labeling processes,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-5, no. 3, pp. 267-287, 1983.
[16] C. A. Kohl, A. Hanson, and E. Riseman, "A goal-directed intermediate level executive for image interpretation," inProc. Tenth Int. Joint Conf. Artificial Intelligence, Milan, Italy, 1987, pp. 811-814.
[17] M. Levine, "A knowledge-based computer vision system," inComputer Vision Systems, A. Hanson and E. Riseman, Eds. New York: Academic, 1978, pp. 335-352.
[18] A. K. Mackworth, "Consistency in networks of relations,"Artificial Intell., vol. 8, no. 1, pp. 99-118, 1977.
[19] A. K. Mackworth, "On reading sketch maps," inProc. Fifth Int. Joint Conf. Artificial Intelligence, Cambridge, 1977, pp. 598-606.
[20] A. K. Mackworth, "Vision research strategy: Black magic, metaphors, mechanisms, miniworlds, and maps," inComputer Vision Systems, A. R. Hanson and E. M. Riseman, Eds. New York: Academic, 1978, pp. 53-60.
[21] A. K. Mackworth, "Constraints, descriptions and domain mappings in computational vision," inPhysical and Biological Processing of Images, O. J. Braddick and A. C. Sleigh, Eds. Berlin, West Germany: Springer-Verlag, 1983, pp. 33-40.
[22] A. K. Mackworth and E. C. Freuder, "The complexity of some polynomial network consistency algorithms for constraint satisfaction problems,"Artificial Intell., vol. 25, pp. 65-74, 1985.
[23] A. K. Mackworth, J. A. Mulder, and W. S. Havens, "Hierarchical arc consistency: Exploiting structured domains in constraint satisfaction problems,"Computational Intell., vol. 1, no. 3-4, pp. 118-126, 1985.
[24] A. K. Mackworth, "Constraint satisfaction," inEncyclopedia of Artificial Intelligence, S. C. Shapiro, Ed. New York: Wiley, 1987, pp. 205-211.
[25] A. K. Mackworth, "Adequacy criteria for visual knowledge representation," Technical Report TR-87-4, Dep. Comput. Sci., Univ. British Columbia, Vancouver, Canada, Tech. Rep. TR-87-4, 1987; to appear inComputational Processes in Human Vision, Z. W. Pylyshyn, Ed. Norwood, NJ: Ablex, to be published.
[26] T. Matsuyama and V. Hwang, "Sigma: A framework for image understanding: Integration of bottom-up and top-down analysis," inProc. Ninth Int. Joint Conf. Artificial Intelligence, Los Angeles, CA, 1985, pp. 908-915.
[27] M. Minsky, "A framework for representing knowledge," inThe Psychology of Computer Vision, P. H. Winston, Ed. New York, McGraw-Hill, 1975, pp. 211-277.
[28] J. A. Mulder and A. K. Mackworth, "Using multi-level semantics to understand sketches of houses and other polyhedral objects," inProc. Second Nat. Conf. Canadian Society for Computational Studies of Intelligence (CSCSI), Toronto, 1978, pp. 244-253.
[29] J. A. Mulder, "Using discrimination graphs to represent visual knowledge," TR-85-14, Dep. Comput. Sci., Univ. British Columbia, Vancouver, 1985.
[30] J. A. Mulder, "Using discrimination graphs to represent visual interpretations that are hypothetical and ambiguous," inProc. Ninth Int. Joint Conf. Artificial Intelligence, Los Angeles, CA, 1985, pp. 905-907.
[31] J. A. Mulder, "Discrimination vision,"Comput. Vision Graphics Image Processing, vol. 43, pp. 313-336, 1988.
[32] J. A. Mulder, "An algorithm which automatically constructs discrimination graphs in a visual knowledge base," inProc. Tenth Int. Joint Conf. Artificial Intelligence, Milan, Italy, 1987, pp. 855-857.
[33] D. Rosenthal and R. Bajesy, "Conceptual and visual focussing in the recognition process as induced by queries," inProc. Fourth Int. Joint Conf. Pattern Recognition, Kyoto, 1978, pp. 417-420.
[34] R. B. Roberts and I. P. Goldstein, "The FRL primer," Artificial Intell. Lab., Massachusetts Inst. Technol., Memo 408, 1977.
[35] D. Sabbah, "Computing with connections in visual recognition of origami objects,"Cognitive Sci., vol. 9, pp. 25-50, 1985.
[36] G. Shafer,A Mathematical Theory of Evidence. Princeton, NJ: Princeton University Press, 1976.
[37] R. C. Shank, "Conceptual dependency: A theory of natural language understanding,"Cognitive Psychol., vol. 3, no. 4, 1972.
[38] M. J. Stefik, "An examination of frame-structured representation systems," inProc. 6th Int. Joint Conf. Artificial Intelligence, Tokyo, 1979, pp. 845-852.
[39] S. Tanimoto and T. Pavlidis, "A hierarchical datastructure for picture processing,"Comput. Graphics Image Processing, vol. 4, pp. 104- 119, 1975.
[40] J. M. Tenenbaum, M. A. Fischler, and H. C. Wolf, "A scene analysis approach to remote sensing," AI Center, S.R.I., Tech. Rep. TN 173, 1978.
[41] J. K. Tsotsos, "Knowledge organization and its role in representation and interpretation for time-varying data: The ALVEN system,"Computational Intell., vol. 1, no. 1, pp. 16-32, 1985.
[42] J. K. Tsotsos, "Image understanding," inEncyclopedia of Artificial Intelligence, S. C. Shapiro, Ed. New York: Wiley, 1987, pp. 389-409.
[43] D. Waltz, "Understanding line drawings of scenes with shadows," inThe Psychology of Computer Vision, P. H. Winston, Ed. New York: McGraw-Hill, 1975, pp. 19-91.
[44] T. E. Weymouth, "Using object descriptions in a schema network for machine vision," Ph.D. dissertation, Dep. Comput. Inform. Sci., Univ. Massachusetts, Amherst, COINS Tech. Rep. 86-24, 1986.
[45] T. Winograd, "Frame representations and the declarative/procedural controversy," inRepresentation and Understanding: Studies in Cognitive Science, D. G. Bobrow and A. Collins, Eds. New York: Academic, 1975, pp. 185-210.

Index Terms:
computer vision; computerized pattern recognition; Mapsee approach; visual knowledge; map interpretation programs; schema-based representation; hierarchical arc-consistency algorithm; discrimination graphs; computer vision; computerised pattern recognition; graph theory; knowledge representation
Citation:
J.A. Mulder, A.K. Mackworth, W.S. Havens, "Knowledge Structuring and Constraint Satisfaction: The Mapsee Approach," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 6, pp. 866-879, Nov. 1988, doi:10.1109/34.9108
Usage of this product signifies your acceptance of the Terms of Use.