상세정보
학술지
Hierarchical Soft Clustering Tree for Fast Approximate Search of Binary Codes
- 저자
- 최수길, 이수원, 양현승
- 발행일
- 201511
- 출처
- Electronics Letters, v.51 no.24, pp.1992-1994
- ISSN
- 0013-5194
- 출판사
- IET
- 초록
- 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 제안 키워드
- Approximate search, Approximation methods, Binary codes, Binary data, Clustering tree, Computer Vision(CV), Hamming space, Hierarchical Clustering, Large-scale datasets, Linear search, Soft Clustering