计算机工程与应用2016，Vol.52Issue(20)：20-25,51,7.DOI:10.3778/j.issn.1002-8331.1602-0013
基于霍夫森林和半监督学习的图像分类
Image classification based on Hough forest and semi-supervised learning
摘要
Abstract
In order to improve the algorithm accuracy, supervised learning algorithms often require a lot of manual anno-tation of samples. Sample labeling process takes a lot of manpower and time. Therefore, how to quickly complete image annotation industry has been a hot research. This paper presents a semi-supervised learning algorithm based on Hough forest, with a relatively small sample of training the classifier, and continues to get new training samples in the classification process, improves the labeling efficiency. The result of algorithm experiments on the dataset of COREL shows that the algorithm can take advantage of a small amount of training samples, satisfactory labeling accuracy.关键词
监督学习/霍夫森林/半监督学习/直推式支持向量机/图像分类Key words
supervised learning/Hough forest/semi-supervised learning/Transductive Support Vector Machine(TSVM)/image classification分类
信息技术与安全科学引用本文复制引用
王力冠,冯瑞..基于霍夫森林和半监督学习的图像分类[J].计算机工程与应用,2016,52(20):20-25,51,7.基金项目
国家科技支撑计划（No.2013BAH09F01）；上海市科委科技创新行动计划（No.14511106900）。 （）
