Affiliations: Beijing Lab of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, China
Correspondence:
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Corresponding author: Yuchai Wan, Beijing Lab of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing 100081, China. Tel.: +86 10 68913447; E-mail:[email protected]
Abstract: In the big data era, the efficient indexing of gradually
increasing databases is becoming vitally important for information
retrieval. To incrementally adapt to changes of databases, in this paper we
propose a novel clustering based dynamic indexing and retrieval approach.
The tree-like indexing structure, termed as CD-Tree, updates the structure
with constant insertion of data, keeping the tree in consistent with the
newest database. The nodes in the CD-Tree are fitted by Gaussian Mixture
Models, based on which we design the efficient updating algorithm. The
similarity retrieval method utilizing the CD-Tree is further presented,
combining one-way search and backtracking strategy to gain good retrieval
accuracy and efficiency. We applied the CD-Tree to example-based image
retrieval. The experimental results confirm that our approach is effective
and promising.
Keywords: Information retrieval, data stream, dynamic indexing, tree-like indexing structure, Gaussian Mixture Models (GMM)
