Retrieved Image Refinement by Bootstrap Outlier Test

Hayato Watanabe, Hideitsu Hino*, Shotaro Akaho, Noboru Murata


研究成果: Conference contribution

1 被引用数 (Scopus)


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.

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
出版ステータスPublished - 2019
イベント18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019 - Salerno, Italy
継続期間: 2019 9月 32019 9月 5


名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11678 LNCS


Conference18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)


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