Self-Organizing Binary Encoding for Approximate Nearest Neighbor Search

Ezgi Can Ozan, Serkan Kiranyaz, Moncef Gabbouj, Xiaohua Hu

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


    Approximate Nearest Neighbor (ANN) search for indexing and retrieval has become very popular with the recent growth of the databases in both size and dimension. In this paper, we propose a novel method for fast approximate distance calculation among the compressed samples. Inspiring from Kohonen’s self-organizing maps, we propose a structured hierarchical quantization scheme in order to compress database samples in a more efficient way. Moreover, we introduce an error correction stage for encoding, which further improves the
    performance of the proposed method. The results on publicly available benchmark datasets demonstrate that the proposed method outperforms many well-known methods with comparable computational cost and storage space.
    Original languageEnglish
    Title of host publication2016 24th European Signal Processing Conference (EUSIPCO)
    Number of pages5
    ISBN (Print)978-0-9928-6265-7
    Publication statusPublished - 2016
    Publication typeA4 Article in conference proceedings
    EventEuropean Signal Processing Conference -
    Duration: 1 Jan 1900 → …

    Publication series

    ISSN (Electronic)2076-1465


    ConferenceEuropean Signal Processing Conference
    Period1/01/00 → …

    Publication forum classification

    • Publication forum level 1


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