Article ID Journal Published Year Pages File Type
531275 Pattern Recognition 2010 7 Pages PDF
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.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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