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Let’s Look at Style: Visual and Spatial Representation and Reasoning in Design

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The Structure of Style

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Abstract

This chapter explores the perception and modeling of style in design relating to visuo-spatial representation and reasoning. We approach this subject via cognitive and contextual considerations significant to the role of style during designing. A designer’s ability to represent and reason about design artifacts visually and spatially allows meaningful “chunks” of design information to be utilized relative to the designer’s task and context. Central to cognitive and contextual notions of style are two issues, namely the level of semantic interpretation, and the comparative method’s degree of contextual sensitivity. This compound problem requires some explicit and cognitively plausible ordering principle and adaptive measure capable of allowing for dependencies in reasoning about similarities. This chapter first investigates the perception of style in relation to these modeling requirements before demonstrating and testing their implementation. We then discuss style in relation to design tasks and how they can be supported via the classification and retrieval of designs from large databases of visuo-spatial information.

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References

  1. Stacey M (2006) Psychological challenges for the analysis of style. Artificial intelligence for engineering design, analysis and manufacturing. Special Issue Understand Represent Reason Style 20(3):167–184

    Google Scholar 

  2. Knight TW (1994) Transformations in design, a formal approach to stylistic change and innovation in the visual arts. Cambridge University Press, Cambridge, MA

    Google Scholar 

  3. Cohn AG (1997) Qualitative spatial representation and reasoning techniques. In: Brewka G, Habel C, Nebel B (eds) Proceedings of KI-97, vol 1303. Springer, Berlin, pp 1–30

    Google Scholar 

  4. Gero JS, Park S-H (1997a) Qualitative representation of shape and space for computer aided architectural design. CAADRIA’97, Hu’s Publisher, Taipei, pp 323–334

    Google Scholar 

  5. Ding L, Gero JS (1998) Emerging representations of Chinese traditional architectural style using genetic engineering. The international conference on artificial intelligence for engineering, HUST Press, Wuhan, pp 493–498

    Google Scholar 

  6. Jupp J, Gero JS (2004) Qualitative representation and reasoning in design: a hierarchy of shape and spatial languages. Visual and spatial reasoning in design III. Key Center of Design Computing and Cognition, University of Sydney, Boston, MA, pp 139–163

    Google Scholar 

  7. Tversky A (1977) Features of similarity. Psychol Rev 84:327–352

    Article  Google Scholar 

  8. Tversky A, Gati I (1982) Similarity, separability, and the triangle inequality. Psychol Rev 89:123–154

    Article  Google Scholar 

  9. Love BC, Sloman SA (1995). Mutability and the determinants of conceptual transformability. Proceedings of 17th annual conference of the cognitive science society, Pittsburgh, PA, pp 654–659

    Google Scholar 

  10. Sloman SA (1996) The empirical case for two systems of reasoning. Psychol Bull 119:3–22

    Article  Google Scholar 

  11. Krumhansl C (1978) Concerning the applicability of geometric models to similarity data: the interrelationship between similarity and spatial density. Psychol Rev 85(5):445–463

    Article  Google Scholar 

  12. Rada R, Mili H, Bicknell E, Blettner M (1989) Development and application of a metric on semantic nets. IEEE Trans Sys Manage Cybern 19(1):17–30

    Article  Google Scholar 

  13. Egenhofer M, Shariff AR (1998) Metric details for natural language spatial relations. ACM Trans Info Sys 16(4):295–321

    Article  Google Scholar 

  14. Miller G, Charles WG (1991) Contextual correlates of semantic similarity. Lang Cogn Process 6:1–28

    Article  Google Scholar 

  15. Medin DL, Goldstone RL, Gentner D (1993) Respects for similarity. Psychol Rev 100: 254–278

    Article  Google Scholar 

  16. Thomas MSC, Mareschal D (1997). Connectionism and psychological notions of similarity. Proceedings of 19th annual conference of the cognitive science society, Erlbaum, London, pp 757–762

    Google Scholar 

  17. Jupp J (2006) Diagrammatic reasoning in design: computational and cognitive studies in similarity assessment. PhD Thesis, Key Center of Design Computing and Cognition, University of Sydney

    Google Scholar 

  18. Jupp J, Gero JS (2006) Visual style: qualitative and context dependant categorization. Artif Intell Eng Des Anal Manuf 20(3):247–266

    Google Scholar 

  19. Goldstone R (1994) Similarity, interactive activation and mapping. J Exp Psychol Learn Mem Cogn 20(1):3–28

    Article  Google Scholar 

  20. Goldstone R (2003) Learning to perceive while perceiving to learn. In: Kimchi R, Behrmann M, Olson C (eds) Perceptual organization in vision: behavioral and neural perspectives. Lawrence Erlbaum, Mahwah, NJ, pp 233–278

    Google Scholar 

  21. Marr D, Nishihara HK (1978) Representation and recognition of the spatial organization of three-dimensional shapes. Proc R Soc B 200:269–294

    Article  Google Scholar 

  22. Wertheimer M (1977) Untersuchungen zur Lehre von der Gestalt. Psychologische Forschung 4:301–350

    Article  Google Scholar 

  23. Tversky B (1999) What does drawing reveal about thinking? Visual and spatial reasoning in design I. Key Center of Design Computing and Cognition, Sydney, pp 271–282

    Google Scholar 

  24. Jupp J, Gero JS (2005) Cognitive studies in similarity assessment: evaluating a neural network model of visuospatial design similarity. International workshop on human behavior in designing – HBiD05, Aix en Provence, France

    Google Scholar 

  25. Schapiro M (1961) Style. In: Philipson M, Gudel PJ (eds) Aesthetics toda. New American Library, New York, NY, pp 137–171

    Google Scholar 

  26. Gould M, Nunes J, Comas D, Egenhofer M, Freundschuh S, Mark D (1996) Formalizing informal geographic information: cross-cultural human subjects testing. Joint European conference and exhibition on geographical information. Barcelona, March 1996, pp 285–294

    Google Scholar 

  27. Knauff M, Rauh R, Schlieder C (1995) Preferred mental models in qualitative spatial reasoning: a cognitive assessment of Allen’s calculus. Proceedings of 17th annual conference of the cognitive science society, Lawrence Erlbaum Associates, Mahwah, NJ, pp 200–205

    Google Scholar 

  28. Gross M, Do E (1995) Drawing analogies – supporting creative architectural design with visual references. 3rd international conference on computational models of creative design, Sydney, pp 37–58

    Google Scholar 

  29. Park S-H, Gero JS (2000) Categorisation of shapes using shape features. In: Gero JS (ed) Artificial intelligence in design ’00. Kluwer, Dordrecht, pp 203–223

    Chapter  Google Scholar 

  30. Davies J, Goel AK (2001) Visual analogy in problem solving. Proceedings of international joint conference on artificial intelligence. Morgan Kaufmann, Seattle, WA, pp 377–382

    Google Scholar 

  31. Gero JS, Kazakov V (2001) Entropic similarity and complexity measures for architectural drawings. Visual and spatial reasoning in design II. Key Center of Design Computing and Cognition, Sydney, pp 147–161

    Google Scholar 

  32. Forbus K, Tomai E, Usher J (2003) Qualitative spatial reasoning for visual grouping in sketches. Proceedings of 16th international workshop on qualitative reasoning, Brasilia, pp 79–86

    Google Scholar 

  33. Burns K (2004) Creature double feature: on style and subject in the art of caricature. AAAI fall symposium on style and meaning in language, art, music, and design. The AAAI Press, Washington, DC

    Google Scholar 

  34. Kohonen T (1995) Self organising maps. Springer, Berlin

    Book  Google Scholar 

  35. Colagrossi A, Sciarrone F, Seccaroni CA (2003) Methodology for automating the classification of works of art using neural networks. Leonardo 36(1):69–69

    Article  Google Scholar 

  36. Chang N-S, Fu K-S (1980) Query by pictorial example. IEEE Trans Soft Eng 6(6):519–524

    Article  Google Scholar 

  37. Salton G, McGill MJ (1983) Introduction to modern information retrieval. McGraw-Hill, New York, NY

    Google Scholar 

  38. Honkela T, Kaski S, Kohonen T, Lagus K (1998) Self-organizing maps of very large document collections: justification for the WEBSOM method. In: Balderjahn I, Mathar R, Schader M (eds) Classification, data analysis, and data highways. Springer, Berlin, pp 245–252

    Chapter  Google Scholar 

  39. Oja E, Lassksonen J, Koskela M, Brandt S (1999) Self-organizing maps for content-based image database retrieval. In: Oja E, Kaski S (eds) Kohonen maps. Elsevier, New York

    Google Scholar 

  40. Laaksonen J, Koskela M, Laakso S, Oja E (2000) PicSOM – content-based image retrieval with self-organizing maps. Pattern Recognit Lett 21(13–14):1199–1207

    Article  MATH  Google Scholar 

  41. Jupp J, Gero JS (2006) Towards computational analysis of style in architectural design. J Am Soc Info Sci 57(5):45–61

    Google Scholar 

  42. Brown K, Sims N, Williams JH, McMahon CA (1995) Conceptual geometric reasoning by the manipulation of models based on prototypes. Artif Intell Eng Des Anal Manuf 9(5): 367–385

    Article  Google Scholar 

  43. Gero JS, Park S-H (1997) Computable feature-based qualitative modelling of shape and space, CAADFutures ’97. Kluwer, Dordrecht, pp 821–830

    Google Scholar 

  44. Martinoli O, Masulli F, Riani M (1988) Algorithmic information of images. In: Cantoni V, Digesu V, Levialdi, S (eds) Image analysis and processing II. Plenum Press, New York, NY, pp 287–293

    Chapter  Google Scholar 

  45. Allen JF (1984) Towards a general theory of action and time. Artif Intell 23:123–154

    Article  MATH  Google Scholar 

  46. Güsgen HW (1989) Spatial reasoning based on Allen’s temporal logic. Technical Report, TR-89–049. International Computer Science Institute, Berkley, CA

    Google Scholar 

  47. Mukerjee A (1989) Getting beneath the geometry: a systematic approach to modelling spatial relations. Proceedings of workshop on model-based reasoning, IJCAI-89, Detroit, MI, pp 140–141

    Google Scholar 

  48. Hall M (2000) Correlation-based feature selection for discrete and numeric class machine learning. Proceedings of 17th international conference on machine learning, Stanford University, Stanford, CA, pp 359–366

    Google Scholar 

  49. Slonim N, Friedman N, Tishby N (2002) Unsupervised document classification using sequential information maximization. Proceedings of SIGIR’02, 25th ACM international conference on research and development of information retrieval, Tampere, Finland. ACM Press, New York, NY

    Google Scholar 

  50. Downton M, Brennan T (1980) Comparing classifications: an evaluation of several coefficient of partition agreement. Proceedings meeting of the classification society, Boulder, CO

    Google Scholar 

  51. Fowlkes E, Mallows C (1983) A method for comparing two hierarchical clusterings. J Am Stat Assoc 78:553–569

    Article  MATH  Google Scholar 

  52. Cox IJ, Miller ML, Omohundro SM, Yianilos PN (1999) Target testing and the PicHunter Bayesian multimedia retrieval system. Proceedings of 3rd forum on research technology advances in digital libraries, Washington, DC, pp 66–75

    Google Scholar 

  53. Simon H (1975) Style in design. In: Eastman C (ed) Spatial synthesis in computer-aided building design. Applied Science, London, pp 287–309

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Technology Sydney, School of the Built Environment, Broadway, NSW, 2007, Australia

    Julie Jupp

  2. Engineering Design Centre, University of Cambridge, Cambridge, UK

    Julie Jupp

  3. Krasnow Institute of Advanced Study and Volgenau School of Information Technology and Engineering, George Mason University, Fairfax, VA, USA

    John Gero

Authors
  1. Julie Jupp
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  2. John Gero
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Correspondence to Julie Jupp .

Editor information

Editors and Affiliations

  1. Dept. Computer Science, Illinois Institute of Technology, West 31st Street 10, Chicago, 60616, Illinois, USA

    Shlomo Argamon

  2. MITRE Corporation, Bedford, 01730, Massachusetts, USA

    Kevin Burns

  3. Music Department, University of California San Diego, Gilman Drive 9500, La Jolla, 92093-0326, California, USA

    Shlomo Dubnov

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Jupp, J., Gero, J. (2010). Let’s Look at Style: Visual and Spatial Representation and Reasoning in Design. In: Argamon, S., Burns, K., Dubnov, S. (eds) The Structure of Style. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12337-5_8

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  • DOI: https://doi.org/10.1007/978-3-642-12337-5_8

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  • Online ISBN: 978-3-642-12337-5

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