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Hierarchical Soft Clustering Tree for Fast Approximate Search of Binary Codes
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- Authors
- S. Choi, S. Lee, H.S. Yang
- Issue Date
- 2015-11
- Citation
- Electronics Letters, v.51, no.24, pp.1992-1994
- ISSN
- 0013-5194
- Publisher
- IET
- Language
- English
- Type
- Journal Article
- Abstract
- 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.
- KSP Keywords
- Approximate search, Approximation methods, Binary codes, Binary data, Clustering tree, Computer Vision(CV), Hamming space, Hierarchical Clustering, Large-scale datasets, Linear search, Soft Clustering