A quadtree-based representation technique for indexing and retrieval of image databases
Currently, several approaches for image indexing based on the quadtrees exist. In this paper, we propose a new organization for image databases combined with the corresponding algorithm for image search by example. The suggested organization uses the quadtrees to splits the database into multi-subsets by adding some extra fields to facilitate the image search. We suggest a centroid partial match algorithm to process the search query. The algorithm selects random points from an image in a circular uniform movement to check for image match. The proposed organization searches a subset of the image database rather than the whole database. It is flexible since the number of subsets in the database is variable. The centroid image algorithm permits the search regardless of the image size. Both the database organization and the centroid algorithm guarantee that the precision and the recall maximum values are achievable.
quadtrees image database image search image retrieval segmentation