Seeded image segmentation for content-based image retrieval application

Jianping Fan; Mathurin Body; Xingquan Zhu; Mohand-Said Hacid; Essam A. El-Kwae

doi:10.1117/12.451087

19 December 2001 Seeded image segmentation for content-based image retrieval application

Jianping Fan, Mathurin Body, Xingquan Zhu, Mohand-Said Hacid, Essam A. El-Kwae

Proceedings Volume 4676, Storage and Retrieval for Media Databases 2002; (2001) https://doi.org/10.1117/12.451087
Event: Electronic Imaging, 2002, San Jose, California, United States

Abstract

Seeded image growing (SRG) algorithm is very attractive for semantic image segmentation but it also suffer from the problems of pixel sorting orders for labeling and automatic seed selection. We design an automatic SRG algorithm, along with a boundary-oriented parallel pixel labeling technique and an automatic seed selection method. In order to support more efficient image access over large-scale database, we suggest a multi-level image database management structure. This framework also supports a concept-oriented image classification via a probabilistic approach. Hierarchical image indexing and summarization are also discussed.

Citation Download Citation

Jianping Fan, Mathurin Body, Xingquan Zhu, Mohand-Said Hacid, and Essam A. El-Kwae "Seeded image segmentation for content-based image retrieval application", Proc. SPIE 4676, Storage and Retrieval for Media Databases 2002, (19 December 2001); https://doi.org/10.1117/12.451087

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