Coaching the exploration and exploitation in active learning for interactive video retrieval
Conventional active learning approaches for interactive video/image retrieval usually assume the query distribution is unknown, as it is difficult to estimate with only a limited number of labeled instances available. Thus, it is easy to put the system in a dilemma whether to explore the feature spa...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 22(2013), 3 vom: 01. März, Seite 955-68 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
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2013
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't |
| Zusammenfassung: | Conventional active learning approaches for interactive video/image retrieval usually assume the query distribution is unknown, as it is difficult to estimate with only a limited number of labeled instances available. Thus, it is easy to put the system in a dilemma whether to explore the feature space in uncertain areas for a better understanding of the query distribution or to harvest in certain areas for more relevant instances. In this paper, we propose a novel approach called coached active learning that makes the query distribution predictable through training and, therefore, avoids the risk of searching on a completely unknown space. The estimated distribution, which provides a more global view of the feature space, can be used to schedule not only the timing but also the step sizes of the exploration and the exploitation in a principled way. The results of the experiments on a large-scale data set from TRECVID 2005-2009 validate the efficiency and effectiveness of our approach, which demonstrates an encouraging performance when facing domain-shift, outperforms eight conventional active learning methods, and shows superiority to six state-of-the-art interactive video retrieval systems |
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| Beschreibung: | Date Completed 18.07.2013 Date Revised 30.01.2013 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2012.2222902 |