Interactive localized content based image retrieval with multiple-instance active learning
Pattern RecognitionPublished 13 March 2009
Dan Zhang, Fei Wang, Zhenwei Shi, Changshui Zhang
Citations56
SJR quartileQ1
SJR score2.06
SNIP2.67
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TL;DR
Two general multiple- instance active learning methods are proposed, multiple-instance active learning with a simple margin strategy (S-MIAL) and multiple- instances activeLearning with fisher information (F-MIAl), and apply them to the active learning in localized content based image retrieval (LCBIR).
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.
Keywords
Computer Science
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