基于集成分类算法的自动图像标注  

Image Annotation Using the Ensemble Learning

作　　者：蒋黎星[1] 侯进[1] 

机构地区：[1]西南交通大学信息科学与技术学院,成都610031

出　　处：《自动化学报》2012年第8期1257-1262,共6页Acta Automatica Sinica

基　　金：教育部留学回国人员科研启动基金;高等学校博士学科点专项科研基金(20090184120022);中央高校基本科研业务费专项资金科技创新项目(SWJTU09CX036)资助~~

摘　　要：基于语义的图像检索技术中,按照图像的语义进行自动标注是一个具有挑战性的工作.本文把图像的自动标注过程转化为图像分类的过程,通过有监督学习对每个图像区域分类并得到相应关键字,实现标注.采用一种快速随机森林(Fast random forest,FRF)集成分类算法,它可以对大量的训练数据进行有效的分类和标注.在基于Corel数据集的实验中,相比经典算法,FRF改善了运算速度,并且分类精度保持稳定.在图像标注方面有很好的应用.Automatic image annotation is an important but highly challenging problem in semantic-based image retrieval. In this paper, we formulate image annotation as a supervised learning image classification problem under the region-based image annotation framework. In region-based image annotation, keywords are usually associated with individual regions in the training data set. This paper presents a novel ensemble fast random rorest algorithm （FRF）, which can classify a large number of training data effectively by bootstrap aggregation （Bagging） algorithm building multiple tree component classifier. The final result is obtained by component classifier voting. The proposed FRF algorithm is experimented on image annotation Corel data sets. Compared to classical algorithms, the FRF accelerates the operation speed of the algorithm, and the classification accuracy remains robust. It has a good application in automatic image annotation system.

关 键 词：自动图像标注 机器学习 集成分类器 快速随机森林算法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]