Interactive localized content based image retrieval with multiple-instance active learning |
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| Authors: | Dan Zhang [Author Vitae] Fei Wang [Author Vitae] [Author Vitae] Changshui Zhang [Author Vitae] |
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| Affiliation: | a State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, P.R. China
b Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, P.R. China |
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| Abstract: | In this paper, we propose two general multiple-instance active learning (MIAL) methods, multiple-instance active learning with a simple margin strategy (S-MIAL) and multiple-instance active learning with fisher information (F-MIAL), and apply them to the active learning in localized content based image retrieval (LCBIR). S-MIAL considers the most ambiguous picture as the most valuable one, while F-MIAL utilizes the fisher information and analyzes the value of the unlabeled pictures by assigning different labels to them. In experiments, we will show their superior performances in LCBIR tasks. |
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| Keywords: | Multiple-instance active learning Fisher information Simple margin Interactive localized content based image retrieval |
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