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

Jérôme da Rugna (1) , Hubert Konik (2)

1 LIGIV - Laboratoire d'Informatique Graphique et d'Ingénierie de la Vision
2 LHC - Laboratoire Hubert Curien

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.

Keywords: content-based image retrieval; meta data; blur detector; hierarchical process; machine learning

Domains: Computer Science [cs]; Image Processing [eess.IV]

Jérôme da Rugna, Hubert Konik. Automatic blur detection for meta-data extraction in content-based retrieval context. SPIE Internet imaging V, Jan 2004, San Jose, United States. pp.285-294. ⟨ujm-00124900⟩