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Information-Theoretic Active Learning for Content-Based Image Retrieval

  • Björn BarzEmail author
  • Christoph Käding
  • Joachim Denzler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11269)

Abstract

We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode active learning method for binary classification, and apply it for acquiring meaningful user feedback in the context of content-based image retrieval. Instead of combining different heuristics such as uncertainty, diversity, or density, our method is based on maximizing the mutual information between the predicted relevance of the images and the expected user feedback regarding the selected batch. We propose suitable approximations to this computationally demanding problem and also integrate an explicit model of user behavior that accounts for possible incorrect labels and unnameable instances. Furthermore, our approach does not only take the structure of the data but also the expected model output change caused by the user feedback into account. In contrast to other methods, ITAL turns out to be highly flexible and provides state-of-the-art performance across various datasets, such as MIRFLICKR and ImageNet.

Keywords

Batch-mode active learning Image retrieval 

Notes

Acknowledgements

This work was supported by the German Research Foundation as part of the priority programme “Volunteered Geographic Information: Interpretation, Visualisation and Social Computing” (SPP 1894, contract number DE 735/11-1).

Supplementary material

480455_1_En_45_MOESM1_ESM.pdf (2.3 mb)
Supplementary material 1 (pdf 2338 KB)

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Friedrich Schiller University JenaJenaGermany

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