DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Hyun Seung Yang | - |
dc.contributor.advisor | 양현승 | - |
dc.contributor.author | Choi, Su Gil | - |
dc.date.accessioned | 2019-08-25T02:47:53Z | - |
dc.date.available | 2019-08-25T02:47:53Z | - |
dc.date.issued | 2018 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=734505&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/265336 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 전산학부, 2018.2,[v, 68 p. :] | - |
dc.description.abstract | Recent studies show that binary codes are a powerful means to perform large-scale image searching and feature matching. Binary codes require significantly less storage space while enabling efficient computation in the Hamming space. At the core of the algorithms that utilize binary codes is the nearest neighbor problem. Although the distance in the Hamming space can be computed efficiently, a linear search against a large-scale dataset creates a bottleneck. Some approximate nearest neighbor (ANN) search algorithms have been proposed to overcome this limitation | - |
dc.description.abstract | the hierarchical clustering tree (HCT) was proved to outperform the well-known locality sensitive hashing. HCT recursively clusters the input binary codes by assigning each binary code to only one cluster, which leads to a quantization error. As a solution to this problem, in this thesis, we propose two algorithms to create hierarchical soft clustering tree (HSCT) by assigning a data point to multiple nearby clusters in the Hamming space. One is a fuzzy hierarchical soft clustering tree (FHSCT) which soft assigns a data point to multiple nearby clusters based on fuzzy membership weight without considering an expected query point. To increase search precision, the other algorithm, query-aware hierarchical soft clustering tree (QAHSCT) is proposed. QAHSCT assigns a binary code to a cluster based on the probability that the query point nearest to the binary code is assigned to the cluster. Because query points are not available when building trees, the exact distance between a query point and cluster center cannot be obtained. Therefore, distance distribution is used instead, which is approximated using Poisson binomial distribution. Experiments show that our proposed methods outperform the previous ANN search methods in terms of search speed without sacrificing search precision. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Binary code▼abinary feature▼afast search▼ahierarchical soft clustering tree▼aapproximate nearest-neighbor search | - |
dc.subject | 이진 코드▼a이진 특징값▼a고속 검색▼a계층적 중복 군집 트리▼a근사 최근접 이웃 탐색 | - |
dc.title | Hierarchical soft clustering tree for fast matching of binary codes | - |
dc.title.alternative | 이진코드의 고속 검색을 위한 계층적 중복 군집 트리 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전산학부, | - |
dc.contributor.alternativeauthor | 최수길 | - |
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