Approximate k-nearest neighbor search based on the earth mover⇔s distance for efficient content-based information retrieval
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jang, M.-H. | - |
dc.contributor.author | Loh, W.-K. | - |
dc.contributor.author | Kim, S.-W. | - |
dc.contributor.author | Won, J.-I. | - |
dc.date.available | 2020-02-27T12:43:45Z | - |
dc.date.created | 2020-02-12 | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/4375 | - |
dc.description.abstract | The Earth Mover's Distance (EMD) is one of the most-widely used distance functions to measure the similarity between two multimedia objects. While providing good search results, the EMD is too much time-consuming to be used in large multimedia databases. To solve the problem, we propose an approximate knearest neighbor (k-NN) search method based on the EMD. First, the proposed method builds an index using the M-tree, a distance-based multi-dimensional index structure, to reduce the disk access overhead. When building the index, we reduce the number of features in the multimedia objects through dimensionality-reduction. When performing the k-NN search on the M-tree, we find a small set of candidates from the disk using the index and then perform the post-processing on them. Second, the proposed method uses the approximate EMD for index retrieval and post-processing to reduce the computational overhead of the EMD. To compensate the errors due to the approximation, the method provides a way of accuracy improvement of the approximate EMD. We performed extensive experiments to show the efficiency of the proposed method. © 2018 Association for Computing Machinery. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery | - |
dc.relation.isPartOf | ACM International Conference Proceeding Series | - |
dc.subject | Cost reduction | - |
dc.subject | Forestry | - |
dc.subject | Image retrieval | - |
dc.subject | Motion compensation | - |
dc.subject | Semantic Web | - |
dc.subject | Semantics | - |
dc.subject | Accuracy Improvement | - |
dc.subject | Computational overheads | - |
dc.subject | Content-based information retrieval | - |
dc.subject | Dimensionality reduction | - |
dc.subject | Earth Mover&apos | - |
dc.subject | s distance | - |
dc.subject | K nearest neighbor queries | - |
dc.subject | Multi-dimensional index structures | - |
dc.subject | Multimedia database | - |
dc.subject | Nearest neighbor search | - |
dc.title | Approximate k-nearest neighbor search based on the earth mover⇔s distance for efficient content-based information retrieval | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.doi | 10.1145/3227609.3227647 | - |
dc.identifier.bibliographicCitation | ACM International Conference Proceeding Series | - |
dc.identifier.scopusid | 2-s2.0-85053484232 | - |
dc.citation.title | ACM International Conference Proceeding Series | - |
dc.contributor.affiliatedAuthor | Loh, W.-K. | - |
dc.type.docType | Conference Paper | - |
dc.subject.keywordAuthor | Content-based information retrieval | - |
dc.subject.keywordAuthor | Earth mover&apos | - |
dc.subject.keywordAuthor | s distance | - |
dc.subject.keywordAuthor | K-nearest neighbor query | - |
dc.subject.keywordPlus | Cost reduction | - |
dc.subject.keywordPlus | Forestry | - |
dc.subject.keywordPlus | Image retrieval | - |
dc.subject.keywordPlus | Motion compensation | - |
dc.subject.keywordPlus | Semantic Web | - |
dc.subject.keywordPlus | Semantics | - |
dc.subject.keywordPlus | Accuracy Improvement | - |
dc.subject.keywordPlus | Computational overheads | - |
dc.subject.keywordPlus | Content-based information retrieval | - |
dc.subject.keywordPlus | Dimensionality reduction | - |
dc.subject.keywordPlus | Earth Mover&apos | - |
dc.subject.keywordPlus | s distance | - |
dc.subject.keywordPlus | K nearest neighbor queries | - |
dc.subject.keywordPlus | Multi-dimensional index structures | - |
dc.subject.keywordPlus | Multimedia database | - |
dc.subject.keywordPlus | Nearest neighbor search | - |
dc.description.journalRegisteredClass | scopus | - |
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