Advertisement

Relative Minimum Distance Between Projected Bags for Improved Multiple Instance Classification

  • José Francisco Ruiz-Muñoz
  • Germán Castellanos-Dominguez
  • Mauricio Orozco-AlzateEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11223)

Abstract

A novel relative minimum distance is introduced that allows improving the dissimilarity-based multiple instance classification. To this end, we apply a previously proposed mapping that brings closer, at least, a single instance from each positive training bag, while the negative-bags instances are driven apart. Our results show an increased classification performance on a broad type of real-world datasets.

Keywords

Multiple Instance Learning Metric Learning Nearest-neighbor rule 

Notes

Acknowledgment

This work is partially supported by “Convocatoria 567 de 2012 - Colciencias”. The authors acknowledge support to attend SISAP 2018 provided by “Convocatoria para la Movilidad Internacional de la Universidad Nacional de Colombia (UNAL) 2017–2018”.

References

  1. 1.
    Amores, J.: Multi-instance classification: review, taxonomy and comparative study. Artif. Intell. 201, 81–105 (2013)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Chen, Y., Bi, J., Wang, J.Z.: MILES: multi-instance learning via embedded instance selection. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 1931–1947 (2006)CrossRefGoogle Scholar
  3. 3.
    Cheplygina, V., Tax, D.M.J., Loog, M.: Dissimilarity-based ensembles for multiple instance learning. IEEE Trans. Neural Netw. Learn. Syst. 27(6), 1379–1391 (2016).  https://doi.org/10.1109/TNNLS.2015.2424254CrossRefGoogle Scholar
  4. 4.
    Guillaumin, M., Verbeek, J., Schmid, C.: Multiple instance metric learning from automatically labeled bags of faces. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 634–647. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15549-9_46CrossRefGoogle Scholar
  5. 5.
    Du, R., Wu, Q., He, X., Yang, J.: MIL-SKDE: multi-instance learning with supervised kernel density estimation. Sig. Process. 93(6), 1471–1484 (2013)CrossRefGoogle Scholar
  6. 6.
    Globerson, A., Roweis, S.T.: Metric learning by collapsing classes. In: NIPS, pp. 451–458 (2006)Google Scholar
  7. 7.
    Diene, O., Bhaya, A.: Conjugate gradient and steepest descent constant modulus algorithms applied to a blind adaptive array. Sig. Process. 90(10), 2835–2841 (2010)CrossRefGoogle Scholar
  8. 8.
    Cheplygina, V., Tax, D.M.J., Loog, M.: Multi-instance learning with bag dissimilarities. Pattern Recognit. 48(1), 264–275 (2015)CrossRefGoogle Scholar
  9. 9.
    Wang, J., Zucker, J.D.: Solving the multiple-instance problem: a lazy learning approach. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 1119–1126. Morgan Kaufmann Publishers Inc. (2000)Google Scholar
  10. 10.
    Jin, R., Wang, S., Zhou, Z.H.: Learning a distance metric from multi-instance multi-label data. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 896–902 (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • José Francisco Ruiz-Muñoz
    • 1
    • 2
  • Germán Castellanos-Dominguez
    • 1
  • Mauricio Orozco-Alzate
    • 1
    Email author
  1. 1.Universidad Nacional de Colombia - Sede ManizalesManizalesColombia
  2. 2.Instituto Tecnológico MetropolitanoMedellínColombia

Personalised recommendations