MASCOT: a mechanism for attention-based scale-invariant object recognition in images

  • The efficient management of large multimedia databases requires the development of new techniques to process, characterize, and search for multimedia objects. Especially in the case of image data, the rapidly growing amount of documents prohibits a manual description of the images’ content. Instead, the automated characterization is highly desirable to support annotation and retrieval of digital images. However, this is a very complex and still unsolved task. To contribute to a solution of this problem, we have developed a mechanism for recognizing objects in images based on the query by example paradigm. Therefore, the most salient image features of an example image representing the searched object are extracted to obtain a scale-invariant object model. The use of this model provides an efficient and robust strategy for recognizing objects in images independently of their size. Further applications of the mechanism are classical recognition tasks such as scene decomposition or object tracking in video sequences.

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Author:Björn Arlt, Rüdiger W. BrauseGND, Erwin Tratar
Parent Title (English):Universität Frankfurt am Main. Fachbereich Informatik: Interner Bericht ; 00,2
Series (Serial Number):Interner Bericht / Fachbereich Informatik, Johann Wolfgang Goethe-Universität Frankfurt a.M. (00, 02)
Publisher:Fachbereich Informatik, Univ.
Place of publication:Frankfurt am Main
Document Type:Working Paper
Year of Completion:2000
Year of first Publication:2000
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2009/07/14
Tag:attention-based object recognition; image databases; scale-invariant object model
Page Number:9
Last Page:9
Institutes:Informatik und Mathematik / Informatik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):License LogoDeutsches Urheberrecht