Improving High-dimensional Indexing with Heuristics for Content-Based Image Retrieval
Abstract
Most high-dimensional indexing structures proposed for sim-ilarity query in content-based image retrieval (CBIR) systems are tree- structured. The quality of a high-dimensional tree-structured index is mainly determined by its insertion algorithm. Our approach focuses on an important phase in insertion, that is, the tree descending phase, when the tree is explored to find a host node to accommodate the vector to be inserted. We propose to integrate a heuristic algorithm in tree descend-ing in order to find a better host node and thus improve the quality of the resulting index. A heuristic criteria for child selection has been de-veloped, which takes into account both the similarity-based distance and the radius-increasing of the potential host node. Our approach has been implemented and tested on an image database. Our experiments show that the proposed approach can improve the quality of high-dimensional indices without much run-time overhead.
Recommended Citation
Y. Fu and J. C. Teng, "Improving High-dimensional Indexing with Heuristics for Content-Based Image Retrieval," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1737, pp. 249 - 267, Springer, Jan 1999.
The definitive version is available at https://doi.org/10.1007/3-540-46621-5_15
Department(s)
Computer Science
International Standard Book Number (ISBN)
978-354066931-9
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Jan 1999
