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

Document Type

Conference Proceeding

Publication Date

12-1-2005

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. © 2005 IEEE.

Identifier

33947132682 (Scopus)

ISBN

[0769526578, 9780769526577]

Publication Title

Proceedings International Workshop on Biomedical Data Engineering Bmde2005

External Full Text Location

https://doi.org/10.1109/ICDE.2005.244

Volume

2005

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