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Joint K-Means quantization for Approximate Nearest Neighbor Search | IEEE Conference Publication | IEEE Xplore

Joint K-Means quantization for Approximate Nearest Neighbor Search


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 ve...Show More

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.
Date of Conference: 04-08 December 2016
Date Added to IEEE Xplore: 24 April 2017
ISBN Information:
Conference Location: Cancun, Mexico

References

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