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Extended Margin and Soft Balanced Strategies in Active Learning

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Advances in Databases and Information Systems (ADBIS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11019))

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Abstract

Nowadays active learning is gaining increasing interest in computer vision community, especially on images. The most commonly used query strategy framework is uncertainty sampling usually in a pool-based sampling scenario. In this paper we propose two query strategies for image classification under the uncertainty sampling framework, both of them being improvements of existing techniques. The first strategy, so called Extended Margin incorporates all possible class labels to calculate the informativeness values of unlabeled instances. The second strategy is the improvement of the recently published BAL method, so called Soft Balanced approach, where we suggest new final informativeness score from an uncertainty measure and a novel penalty metric. We used least margin criterion for the former and the latter was calculated from the categorical penalty scores by using soft assignment. We conducted experiments on 60 different test image sets, each of them was a randomly selected subset of the Caltech101 image collection. The experiments were performed in an extended active learning environment and the results showed that the Extended Margin outperforms the least margin approach and the Soft Balanced method overcomes all other competitor method.

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Acknowledgement

The research has been supported by the European Union, co-financed by the European Social Fund (EFOP-3.6.2-16-2017-00013).

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Authors and Affiliations

  1. Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Magyar Tudósok krt. 2, Budapest, 1117, Hungary

    Dávid Papp & Gábor Szűcs

Authors
  1. Dávid Papp
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  2. Gábor Szűcs
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Correspondence to Dávid Papp .

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Editors and Affiliations

  1. Eötvös Loránd University, Budapest, Hungary

    András Benczúr

  2. Christian-Albrechts-Universität, Kiel, Germany

    Bernhard Thalheim

  3. Eötvös Loránd University, Budapest, Hungary

    Tomáš Horváth

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Papp, D., Szűcs, G. (2018). Extended Margin and Soft Balanced Strategies in Active Learning. In: Benczúr, A., Thalheim, B., Horváth, T. (eds) Advances in Databases and Information Systems. ADBIS 2018. Lecture Notes in Computer Science(), vol 11019. Springer, Cham. https://doi.org/10.1007/978-3-319-98398-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-98398-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98397-4

  • Online ISBN: 978-3-319-98398-1

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