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This paper presents the AH+-tree, a balanced, tree-based index structure that efficiently supports Content-Based Image Retrieval (CBIR) through similarity queries. The proposed index structure addresses the problems of semantic gap and user subjectivity by considering the high-level semantics of multimedia data during the retrieval process. The AH+-tree provides the same functionality as the Affinity-Hybrid Tree (AH-Tree) but utilizes the high-level semantics in a novel way to eliminate the I/O overhead incurred by the AH-Tree due to the process of affinity propagation, which requires a complete traversal of the tree. The novel structure of the tree is explained, and detailed range and nearest neighbor algorithms are implemented and analyzed. Extensive discussions and experiments demonstrate the superior efficiency of the AH+-tree over the AH-Tree and the M-tree. Results show the AH+-tree significantly reduces I/O cost during similarity searches. The I/O efficiency of the AH+-tree and its ability to incorporate high-level semantics from different machine learning mechanisms make the AH+-tree a promising index access method for large multimedia databases.