Abstract
Multiple Instance Learning (MIL) has become a prominent framework for image categorization problem. In MIL framework, images are described as the combination of multiple regions. Active learning in MIL framework becomes useful when large amount of unlabeled data is available and labeling is too costly to handle for each unlabeled data. In the literature, there are some researches on MI active learning but none of them take advantage of the ensemble techniques and sparse coding. In this work, we study a Dictionary Ensemble based MI Active Learning method. Experiments show that the proposed algorithm has higher classification accuracy over other techniques.
Translated title of the contribution | Dictionary ensemble based multi instance active learning method for image categorization |
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Original language | Turkish |
Title of host publication | 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1221-1224 |
Number of pages | 4 |
ISBN (Electronic) | 9781509016792 |
DOIs | |
Publication status | Published - 20 Jun 2016 |
Event | 24th Signal Processing and Communication Application Conference, SIU 2016 - Zonguldak, Turkey Duration: 16 May 2016 → 19 May 2016 |
Publication series
Name | 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings |
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Conference
Conference | 24th Signal Processing and Communication Application Conference, SIU 2016 |
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Country/Territory | Turkey |
City | Zonguldak |
Period | 16/05/16 → 19/05/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.