A SEMANTIC INDEX STRUCTURE FOR MULTIMEDIA RETRIEVAL

To be effective multimedia retrieval mechanisms, index methods must provide not only efficient access but also meaningful retrieval by addressing challenges in multimedia retrieval. This article presents the AH+-tree, a height-balanced, tree-based index structure that efficiently incorporates high-level affinity information to support Content-Based Image Retrieval (CBIR) through similarity queries. The incorporation of affinity information allows the AH+-tree to address the problems of semantic gap and user perception subjectivity inherent to multimedia retrieval. Based on the Affinity-Hybrid Tree (AH-Tree), the AH+-tree utilizes affinity information in a novel way to eliminate the I/O overhead of the AH-Tree while maintaining the same functionality and quality of results. We explain the structure of the AH+-tree and implement and analyze algorithms for tree construction and similarity queries (range and nearest neighbor). Experimental results demonstrate the superior I/O efficiency of the AH+-tree over that of the AH-Tree and the M-tree without a detrimental impact on real-time costs of the retrieval process.