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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%.
|Title of host publication||Proceedings - International Symposium on Biomedical Imaging|
|Publisher||IEEE Computer Society|
|Number of pages||4|
|Publication status||Published - 2015|
|Event||12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States|
Duration: 16 Apr 2015 → 19 Apr 2015
|Conference||12th IEEE International Symposium on Biomedical Imaging, ISBI 2015|
|Period||16/04/15 → 19/04/15|
- Colonoscopy image classification
- Discriminative feature learning
- Local Binary Patterns
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