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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