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Authors: Naoya Higuchi 1 ; Yasunobu Imamura 1 ; Tetsuji Kuboyama 2 ; Kouichi Hirata 1 and Takeshi Shinohara 1

Affiliations: 1 Kyushu Institute of Technology, Japan ; 2 Gakushuin University, Japan

ISBN: 978-989-758-276-9

Keyword(s): Similarity Search, Sketches, Ball Partitioning, Hamming Distance, Dimension Reduction, Distance Lower Bound, Quantized Images.

Related Ontology Subjects/Areas/Topics: Applications ; Data Engineering ; Ensemble Methods ; Information Retrieval ; Ontologies and the Semantic Web ; Pattern Recognition ; Similarity and Distance Learning ; Software Engineering ; Theory and Methods

Abstract: In this paper, we discuss sketches based on ball partitioning (BP), which are compact bit sequences representing multidimensional data. The conventional nearest search using sketches consists of two stages. The first stage selects candidates depending on the Hamming distances between sketches. Then, the second stage selects the nearest neighbor from the candidates. Since the Hamming distance cannot completely reflect the original distance, more candidates are needed to achieve higher accuracy. On the other hand, we can regard BP sketches as quantized images of a dimension reduction. Although quantization error is very large if we use only sketches to compute distances, we can partly recover distance information using query. That is, we can compute a lower bound of distance between a query and a data using only query and the sketch of the data. We propose candidate selection methods at the first stage using the lower bounds. Using the proposed method, higher level of accuracy f or nearest neighbor search is shown through experimenting on multidimensional data such as images, music and colors. (More)

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Paper citation in several formats:
Higuchi, N.; Imamura, Y.; Kuboyama, T.; Hirata, K. and Shinohara, T. (2018). Nearest Neighbor Search using Sketches as Quantized Images of Dimension Reduction.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 356-363. DOI: 10.5220/0006585003560363

@conference{icpram18,
author={Naoya Higuchi. and Yasunobu Imamura. and Tetsuji Kuboyama. and Kouichi Hirata. and Takeshi Shinohara.},
title={Nearest Neighbor Search using Sketches as Quantized Images of Dimension Reduction},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={356-363},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006585003560363},
isbn={978-989-758-276-9},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Nearest Neighbor Search using Sketches as Quantized Images of Dimension Reduction
SN - 978-989-758-276-9
AU - Higuchi, N.
AU - Imamura, Y.
AU - Kuboyama, T.
AU - Hirata, K.
AU - Shinohara, T.
PY - 2018
SP - 356
EP - 363
DO - 10.5220/0006585003560363

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