| 张晓宇.基于多视角二维主动学习的多标签分类[J].高技术通讯(中文),2011,21(12):1312~1317 |
| 基于多视角二维主动学习的多标签分类 |
| Multi label classification based on multi view two dimensional active learning |
| 修订日期：2010-03-11 |
| DOI： |
| 中文关键词: 主动学习（AL）， 多视角学习， 多标签分类， 图像分类， 多模态融合 |
| 英文关键词: active learning (AL), multi view learning, multi label classification, image classification, multi model fusion |
| 基金项目:中央级公益性科研院所基本科研业务费专项资金（ZD2011 7 3）和中国科学技术信息研究所科研预研基金（YY 201114）资助项目 |
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| 中文摘要: |
| 针对多标签图像分类问题的特点，提出了一种多视角二维主动学习（MV 2DAL）算法，以通过多视角学习与主动学习的有机结合，深入挖掘样本、标签、视角三个维度上的相关性和冗余性。此算法以样本 标签对作为基本标注单位，在每个视角内，利用二维主动学习的方法计算样本、标签维度上的不确定度；在不同视角间，通过多视角融合的方法计算跨视角的不确定度；最终，将视角内不确定度与视角间不确定度进行融合得到总不确定度，并以此衡量样本 标签对的标注价值。将MV 2DAL算法应用到图像内容理解的一个重要领域——多标签图像分类中，显著提 |
| 英文摘要: |
| This paper presents the multi view two dimensional active learning (MV 2DAL) algorithm for multi label image classification so as to thoroughly explore the redundancies along the dimensions of sample, label and view, by the organic integration of the active learning with the multi view learning. Taking a sample label pair as the basic labeling unit, the algorithm calculates the uncertainties from the dimensions of sample and label within each view using the two dimensional active learning, and captures the uncertainties over different views based on the multi view fusion. The overall uncertainty along the three dimensions is obtained to detect the most informative sample label pairs. The experiments on the real world multi label image classification demonstrate that the proposed MV 2DAL algorithm is effective for redundancy reduction, and thus greatly alleviates the burden on human labeling |
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