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Multi-Dimensional Image Indexing with R-Tree

In traditional approach used for image indexing is typically compute a single signature for each image based on color histograms, texture, wavelet transforms, region based etc, and return as the query result, image whose coordinates match closet to the inserted query image. Therefore, sometime it becomes very hard to match the similar coordinates when the objects that scaled differently or at different locations or only certain regions of the image match. The research aspects of image feature representation and extraction, multi-dimensional indexing and based on the Content-Based Image Retrieval. Second, a variation to Guttman’s R-trees (R-trees) that avoids overlapping rectangles in intermediate nodes of the tree is intro duced. Algorithms for searching, updating, initial packing and reorganization of the structure are discussed in detail. Finally, we provide analytical results indicating that R-trees achieve up to 50% savings in disk accesses compared to an R-tree when searching files of thousands of rectangles.