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

Jerome Da Rugna; Hubert Konik

doi:10.1117/12.526949

15 December 2003 Automatic blur detection for meta-data extraction in content-based retrieval context

Jerome Da Rugna, Hubert Konik

Proceedings Volume 5304, Internet Imaging V; (2003) https://doi.org/10.1117/12.526949
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

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.

Citation Download Citation

Jerome Da Rugna and Hubert Konik "Automatic blur detection for meta-data extraction in content-based retrieval context", Proc. SPIE 5304, Internet Imaging V, (15 December 2003); https://doi.org/10.1117/12.526949

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