Object categorization and the need for many-to-many matching

Creative Commons License

Dickinson S., Shokoufandeh A., Keselman Y., Demirci F., Macrini D.

27th DAGM (German Association for Pattern Recognition) Symposium, DAGM 2005, Vienna, Austria, 31 August - 02 September 2005, vol.3663, pp.501-510 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 3663
  • Doi Number: 10.1007/11550518_62
  • City: Vienna
  • Country: Austria
  • Page Numbers: pp.501-510
  • Ankara Yıldırım Beyazıt University Affiliated: No


Object recognition systems have their roots in the AI community, and originally addressed the problem of object categorization. These early systems, however, were limited by their inability to bridge the representational gap between low-level image features and high-level object models, hindered by the assumption of one-to-one correspondence between image and model features. Over the next thirty years, the mainstream recognition community moved steadily in the direction of exemplar recognition while narrowing the representational gap. The community is now returning to the categorization problem, and faces the same representational gap as its predecessors did. We review the evolution of object recognition systems and argue that bridging this representational gap requires an ability to match image and model features many-to-many. We review three formulations of the many-to-many matching problem as applied to model acquisition and object recognition. © Springer-Verlag Berlin Heidelberg 2005.