Joint K-Means Quantization for Approximate Nearest Neighbor Search

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    1 Citation (Scopus)


    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.
    Original languageEnglish
    Title of host publication23rd International Conference on Pattern Recognition (ICPR 2016)
    ISBN (Electronic)978-1-5090-4847-2
    Publication statusPublished - 2017
    Publication typeA4 Article in conference proceedings
    EventInternational Conference on Pattern Recognition -
    Duration: 1 Jan 1900 → …


    ConferenceInternational Conference on Pattern Recognition
    Period1/01/00 → …

    Publication forum classification

    • Publication forum level 1


    Dive into the research topics of 'Joint K-Means Quantization for Approximate Nearest Neighbor Search'. Together they form a unique fingerprint.

    Cite this