Retrieved Image Refinement by Bootstrap Outlier Test

Hayato Watanabe, Hideitsu Hino, Shotaro Akaho, Noboru Murata

研究成果: Conference contribution

抄録

Outlier detection is used to identify data points or a small number of subsets of data that are significantly different from most other data in a given dataset. It is challenging to detect outliers using an objective and quantitative approach. Methods that use the framework of statistical hypothesis testing are widely used by assuming a specific parametric distribution as a data generation model, but there is no guarantee that the distribution of data can be adequately approximated by a parametric distribution in practical problems. In this paper, a simple method is proposed to objectively detect outliers by hypothesis testing without assuming a specific distribution of outlier scores. By using an arbitrary outlier score function, hypothesis testing is used to determine whether each given sample is an outlier. The distribution of the test statistics is needed for the hypothesis test, and is estimated based on the given data using the bootstrap method. The effectiveness of the proposed outlier test was verified by applying it to outlier detection for text-based image retrieval, where it improved the quality of image searches by removing irrelevant images.

本文言語English
ホスト出版物のタイトルComputer Analysis of Images and Patterns - 18th International Conference, CAIP 2019, Proceedings
編集者Mario Vento, Gennaro Percannella
出版社Springer-Verlag
ページ505-517
ページ数13
ISBN(印刷版)9783030298876
DOI
出版ステータスPublished - 2019 1 1
イベント18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019 - Salerno, Italy
継続期間: 2019 9 32019 9 5

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11678 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019
CountryItaly
CitySalerno
Period19/9/319/9/5

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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