Approximate k-nearest neighbor search based on the earth mover⇔s distance for efficient content-based information retrieval
- Authors
- Jang, M.-H.; Loh, W.-K.; Kim, S.-W.; Won, J.-I.
- Issue Date
- 2018
- Publisher
- Association for Computing Machinery
- Keywords
- Content-based information retrieval; Earth mover' s distance; K-nearest neighbor query
- Citation
- ACM International Conference Proceeding Series
- Journal Title
- ACM International Conference Proceeding Series
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/4375
- DOI
- 10.1145/3227609.3227647
- ISSN
- 0000-0000
- 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.
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