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INDEXING IMAGES WITH MULTIPLE REGIONS

Similarity indexing using Spatial Access Methods (SAMs) like e.g., R-trees, assumes that each data entity (or query) is represented by exactly one multidimensional point. However, for several applications, including indexing and retrieval of multimedia data like onedimensional signals and images, it is required that each data entity is represented by multiple points in a multidimensional space. This work extends the existing framework of indexing using SAMs to handle such data entities and has many desirable properties: For example, it provides index support for the two most common types of similarity queries, namely range and nearest neighbor (NN) queries, it returns exactly the same answers with the sequential scan method (only much faster) and, it works with any SAM and any data type. The effectiveness of the proposed approach is demonstrated using images with multiple regions.