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Proceedings Paper

Automatic blur detection for meta-data extraction in content-based retrieval context
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Paper Abstract

During the last few years, image by content retrieval is the aim of many studies. A lot of systems were introduced in order to achieve image indexation. One of the most common method is to compute a segmentation and to extract different parameters from regions. However, this segmentation step is based on low level knowledge, without taking into account simple perceptual aspects of images, like the blur. When a photographer decides to focus only on some objects in a scene, he certainly considers very differently these objects from the rest of the scene. It does not represent the same amount of information. The blurry regions may generally be considered as the context and not as the information container by image retrieval tools. Our idea is then to focus the comparison between images by restricting our study only on the non blurry regions, using then these meta data. Our aim is to introduce different features and a machine learning approach in order to reach blur identification in scene images.

Paper Details

Date Published: 15 December 2003
PDF: 10 pages
Proc. SPIE 5304, Internet Imaging V, (15 December 2003); doi: 10.1117/12.526949
Show Author Affiliations
Jerome Da Rugna, Lab. LIGIV/Univ. Jean Monnet (France)
Hubert Konik, Lab. LIGIV/Univ. Jean Monnet (France)


Published in SPIE Proceedings Vol. 5304:
Internet Imaging V
Simone Santini; Raimondo Schettini, Editor(s)

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