Multi-level index for global and partial content-based image retrieval

Geneviève Jomier, Maude Manouvrier, Vincent Oria, Marta Rukoz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

This article presents a quadtree-based data structure for effective indexing of images. An image is represented by a multi-level feature vector, computed by a recursive decomposition of the image into four quadrants and stored as a full fixed-depth balanced quadtree, A node of the quadtree stores a feature vector of the corresponding image quadrant, A more general quadtree-based structure called QUIP-tree (QUadtree-based Index for image retrieval and Pattern search) is used to index the multi-level feature vectors of the images and their quadrants. A QUIP-tree node is an entry to a set of clusters that groups similar quadrants according to some pre-defined distances. The QUIP-tree allows a multi-level filtering in content-based image retrieval as well as partial queries on images.

Original languageEnglish (US)
Title of host publicationProceedings - International Workshop on Biomedical Data Engineering, BMDE2005
DOIs
StatePublished - 2005
Event21st International Conference on Data Engineering Workshops 2005 - Tokyo, Japan
Duration: Apr 3 2005Apr 4 2005

Publication series

NameProceedings - International Workshop on Biomedical Data Engineering, BMDE2005
Volume2005

Other

Other21st International Conference on Data Engineering Workshops 2005
Country/TerritoryJapan
CityTokyo
Period4/3/054/4/05

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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