Learning discriminative local features from image-level labelled data for colonoscopy image classification

Siyamalan Manivannan, Emanuele Trucco

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

4 Citations (Scopus)

Abstract

In this paper we propose a novel weakly-supervised feature learning approach, learning discriminative local features from image-level labelled data for image classification. Unlike existing feature learning approaches which assume that a set of additional data in the form of matching/non-matching pairs of local patches are given for learning the features, our approach only uses the image-level labels which are much easier to obtain. Experiments on a colonoscopy image dataset with 2100 images shows that the learned local features outperforms other hand-crafted features and gives a state-or-the-art classification accuracy of 93.5%.

Original languageEnglish
Title of host publicationProceedings - International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages420-423
Number of pages4
Volume2015-July
ISBN (Print)9781479923748
DOIs
Publication statusPublished - 2015
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: 16 Apr 201519 Apr 2015

Conference

Conference12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
CountryUnited States
CityBrooklyn
Period16/04/1519/04/15

Keywords

  • Colonoscopy image classification
  • Discriminative feature learning
  • Local Binary Patterns

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    Manivannan, S., & Trucco, E. (2015). Learning discriminative local features from image-level labelled data for colonoscopy image classification. In Proceedings - International Symposium on Biomedical Imaging (Vol. 2015-July, pp. 420-423). [7163901] IEEE Computer Society. https://doi.org/10.1109/ISBI.2015.7163901