MULTI-VIEW MULTI-LABEL ACTIVE LEARNING FOR IMAGE CLASSIFICATION

被引：0

作者：

Zhang, Xiaoyu ^{[1
]}

Cheng, Jian ^{[1
]}

Xu, Changsheng ^{[1
]}

Lu, Hanqing ^{[1
]}

Ma, Songde ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China

来源：

ICME: 2009 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-3 | 2009年

关键词：

Active learning; Multi-view learning; Image classification; Multi-label classification; Multi-view fusion;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Image classification is an important topic in multimedia analysis, among which multi-label image classification is a very challenging task with respect to the large demand for human annotation of multi-label samples. In this paper, we propose a multi-view multi-label active learning strategy, which integrates the mechanism of active learning and multi-view learning. On one hand we explore the sample and label uncertainties within each view; on the other hand we capture the uncertainty over different views based on multi-view fusion. Then the overall uncertainty along the sample, label and view dimensions are obtained to detect the most informative sample-label pairs. Experimental results demonstrate the effectiveness of the proposed scheme.

引用

页码：258 / 261

页数：4

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