自动化学报2011，Vol.37Issue(8)：954-962,9.DOI:10.3724/SP.J.1004.2011.00954
基于主动学习和半监督学习的多类图像分类
Multi-class Image Classification with Active Learning and Semi-supervised Learning
摘要
Abstract
Most image classification methods require adequate labeled training samples to train classifier models. In real world applications, labelling samples are often very time consuming and expensive, especially for some special images, e.g. Synthetic aperture radar (SAR) images. So the number of labeled samples is usually limited. In this study, we propose a novel image classification method based on SVMs, incorporating best vs second-best (BvSB) active learning and constrained self-training (CST). In this method, BvSB active learning is used to explore examples that are the most valuable to current classifier model for manual labelling. And CST is used to exploit useful information from examples that remain in the unlabeled dataset. With this new method, satisfying classification performance can be achieved while the human labelling load is low. We demonstrate results on 3 optical image datasets and a SAR image dataset. The proposed method gives large reduction in the number of human labeled samples as compared with random selection, entropy based active learning and BvSB active learning to achieve similar classification accuracy, and has little computational overhead and good robustness.关键词
主动学习/半监督学习/支持向量机/图像分类Key words
Active learning, semi-supervised learning, support vector machines (SVM), image classification引用本文复制引用
陈荣,曹永锋,孙洪..基于主动学习和半监督学习的多类图像分类[J].自动化学报,2011,37(8):954-962,9.基金项目
国家高技术研究发展计划(863计划)(2007AA12Z155),国家自然科学基金(40901207),测绘遥感信息工程国家重点实验室专项科研经费,中央高校基本科研业务费专项资金资助 （863计划）
