3D model-based still image object categorization

Raluca-Diana Petre; Titus Zaharia

doi:10.1117/12.904964

9 September 2011 3D model-based still image object categorization

Raluca-Diana Petre, Titus Zaharia

Proceedings Volume 8136, Mathematics of Data/Image Pattern Coding, Compression, and Encryption with Applications XIII; 81360C (2011) https://doi.org/10.1117/12.904964
Event: SPIE Optical Engineering + Applications, 2011, San Diego, California, United States

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.

Citation Download Citation

Raluca-Diana Petre and Titus Zaharia "3D model-based still image object categorization", Proc. SPIE 8136, Mathematics of Data/Image Pattern Coding, Compression, and Encryption with Applications XIII, 81360C (9 September 2011); https://doi.org/10.1117/12.904964

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