Adaptively browsing image databases with PIBE
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Browsing large image collections is a complex and often tedious task, due to the semantic gap existing between the user subjective notion of similarity and the one according to which a browsing system organizes the images. In this paper we propose PIBE, an adaptive image browsing system, which provides users with a hierarchical view of images (the Browsing Tree) that can be customized according to user preferences. A key feature of PIBE is that it maintains local similarity criteria for each portion of the Browsing Tree. This makes it possible both to avoid costly global reorganization upon execution of user actions and, combined with a persistent storage of the Browsing Tree, to efficiently support multiple browsing tasks. We present the basic principles of PIBE and report experimental results showing the effectiveness of its browsing and personalization functionalities.
KeywordsImage databases Browsing Personalization Similarity criteria
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- 1.Bartolini I, Ciaccia P, Patella M (2004) The PIBE personalizable image browsing engine. In: Proc. of the 1st international workshop on computer vision meets databases (CVDB 2004), Paris, France, pp. 43–50Google Scholar
- 2.Bartolini I, Ciaccia P, Waas F (2001) FeedbackBypass: a new approach to interactive similarity query processing. In: Proc. of the 27th international conference on very large data bases (VLDB 2001), Rome, Italy, pp. 201–210Google Scholar
- 4.Chen J, Bouman C, Dalton J (1999) Active browsing using similarity pyramids. In: Proc. of international conference on storage and retrieval for image and video databases (SPIE 1999), San Jose, California, pp. 144–154Google Scholar
- 5.Combs TTA, Bederson BB (1999) Does zooming improve image browsing?. In: Proc. of the fourth ACM conference on digital libraries, Berkeley, California, pp. 130–137 (1999) Google Scholar
- 6.Graham A, Garcia-Molina H, Paepcke A, Winograd T (2002) Time as essence for photo browsing through personal digital libraries. In: Proc. ofACM/IEEE joint conference on digital libraries (JCDL 2002), Portland, Oregon, USA, pp. 326–335Google Scholar
- 8.Laaksonen J, Koskela M, Laakso S, Oja E (2000) Self-organising maps as a relevance feedback technique in content-based image retrieval. Pattern Anal Appl 2(4):140–152Google Scholar
- 9.Loui AC, Wood MD (1999) A software system for automatic albuming of consumer pictures. In: Proc. of the 7th ACM international conference on multimedia ’99, Orlando, Florida, USA, pp. 159–162Google Scholar
- 10.Platt JC, Czerwinski M, Field BA (2003) PhotoTOC: automatic clustering for browsing personal photographs. In: Proc. of the 4th IEEE pacific rim conference on multimedia vol 1, Singapore, pp. 6–10Google Scholar
- 11.Rodden K, Basalaj W, Sinclair D, Wood K (2001) Does organisation by similarity assist image browsing?. In: Proc. of the SIG-CHI human factors in computing systems (CHI 2001), Seattle, Washington, USA, pp. 190–197Google Scholar
- 12.Rubner Y, Tomasi C, Guibas LJ (1998) A metric for distributions with applications to image databases. In: Proc. of the 6th international conference on computer vision (ICCV 1998), Mumbai, India, pp. 59–66Google Scholar