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

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...

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Bibliographic Details
Main Authors: Jomier, G., Manouvrier, M., Oria, V., Rukoz, M.
Format: Conference Proceeding
Language:English
Subjects:
Content based retrieval
Data structures
Educational institutions
Filtering
Image databases
Image retrieval
Information retrieval
Pixel
Spatial databases
Visual databases
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Summary: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.
DOI:10.1109/ICDE.2005.244