Joint K-Means quantization for Approximate Nearest Neighbor Search
| Author | Ozan, Ezgi Can |
| Author | Kiranyaz, Serkan |
| Author | Gabbouj, Moncef |
| Available date | 2021-07-01T05:48:09Z |
| Publication Date | 2016 |
| Publication Name | Proceedings - International Conference on Pattern Recognition |
| Resource | Scopus |
| ISSN | 1051-4651 |
| Abstract | Recently, Approximate Nearest Neighbor (ANN) Search has become a very popular approach for similarity search on large-scale datasets. In this paper, we propose a novel vector quantization method for ANN, which introduces a joint multi-layer K-Means clustering solution for determination of the codebooks. The performance of the proposed method is improved further by a joint encoding scheme. Experimental results verify the success of the proposed algorithm as it outperforms the state-of-the-art methods. |
| Language | en |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Subject | Pattern recognition Vector quantization Approximate nearest neighbors (ANN) Codebooks Joint encoding K - means clustering K-means Large-scale datasets Similarity search State-of-the-art methods Nearest neighbor search |
| Type | Conference |
| Pagination | 3645-3649 |
| Volume Number | 0 |
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