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Proceedings Paper

3D model-based still image object categorization
Author(s): Raluca-Diana Petre; Titus Zaharia
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Paper Abstract

This paper proposes a novel recognition scheme algorithm for semantic labeling of 2D object present in still images. The principle consists of matching unknown 2D objects with categorized 3D models in order to infer the semantics of the 3D object to the image. We tested our new recognition framework by using the MPEG-7 and Princeton 3D model databases in order to label unknown images randomly selected from the web. Results obtained show promising performances, with recognition rate up to 84%, which opens interesting perspectives in terms of semantic metadata extraction from still images/videos.

Paper Details

Date Published: 10 September 2011
PDF: 10 pages
Proc. SPIE 8136, Mathematics of Data/Image Pattern Coding, Compression, and Encryption with Applications XIII, 81360C (10 September 2011); doi: 10.1117/12.904964
Show Author Affiliations
Raluca-Diana Petre, Institut Télécom (France)
MAP5, CNRS, Univ. Paris Descartes (France)
Titus Zaharia, Institut Télécom (France)
MAP5, CNRS, Univ. Paris Descartes (France)


Published in SPIE Proceedings Vol. 8136:
Mathematics of Data/Image Pattern Coding, Compression, and Encryption with Applications XIII
Mark S. Schmalz; Gerhard X. Ritter; Junior Barrera; Jaakko T. Astola, Editor(s)

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