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Multilevel Index for Global and Partial Content-Based Image Retrieval

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Date
2005
Dewey
Programmation, logiciels, organisation des données
Sujet
cluster; quadtree; Image content-based retrieval
Conference country
JAPAN
Book title
21st International Conference on Data Engineering Workshops, 2005. Proceedings
Publisher
IEEE - Institute of Electrical and Electronics Engineers
Publisher city
Piscataway, NJ
Year
2005
ISBN
0-7695-2657-8
URI
https://basepub.dauphine.fr/handle/123456789/5995
Collections
  • LAMSADE : Publications
Metadata
Show full item record
Author
Manouvrier, Maude
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Rukoz, Marta
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Oria, Vincent
Jomier, Geneviève
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Type
Communication / Conférence
Item number of pages
1176
Abstract (EN)
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 corre sponding image quadrant. A more general quadtree-based structure called QUIP-tree (QUadtree-based Index f or 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 som e pre-defined distances. The QUIP-tree allows a multi-level filtering in content-based image retrieval as well as partial queries on images.

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