Hierarchical soft clustering tree for fast approximate search of binary codes

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Binary codes play an important role in many computer vision applications. They require less storage space while allowing efficient computations. However, a linear search to find the best matches among binary data creates a bottleneck for large-scale datasets. Among the approximation methods used to solve this problem, the hierarchical clustering tree (HCT) method is a state-of the-art method. However, the HCT performs a hard assignment of each data point to only one cluster, which leads to a quantisation error and degrades the search performance. As a solution to this problem, an algorithm to create hierarchical soft clustering tree (HSCT) by assigning a data point to multiple nearby clusters in the Hamming space is proposed. Through experiments, the HSCT is shown to outperform other existing methods.
Publisher
INST ENGINEERING TECHNOLOGY-IET
Issue Date
2015-11
Language
English
Article Type
Article
Citation

ELECTRONICS LETTERS, v.51, no.24, pp.1992 - 1993

ISSN
0013-5194
DOI
10.1049/el.2015.2806
URI
http://hdl.handle.net/10203/205553
Appears in Collection
CS-Journal Papers(저널논문)
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