Retrieved Image Refinement by Bootstrap Outlier Test

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (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.

Original languageEnglish
Title of host publicationComputer Analysis of Images and Patterns - 18th International Conference, CAIP 2019, Proceedings
EditorsMario Vento, Gennaro Percannella
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783030298876
Publication statusPublished - 2019
Event18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019 - Salerno, Italy
Duration: 2019 Sept 32019 Sept 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11678 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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


  • Hypothesis testing
  • Image retrieval
  • Outlier removal

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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