基于集成分类算法的自动图像标注

蒋黎星; 侯进

doi:10.3724/SP.J.1004.2012.01257

基于集成分类算法的自动图像标注

doi: 10.3724/SP.J.1004.2012.01257

蒋黎星,
侯进

1.
西南交通大学信息科学与技术学院成都 610031

详细信息

通讯作者:
侯进

计量
- 文章访问数: 2435
- HTML全文浏览量: 74
- PDF下载量: 1600
- 被引次数: 0
出版历程
- 收稿日期: 2011-05-30
- 修回日期: 2011-07-04
- 刊出日期: 2012-08-20

Image Annotation Using the Ensemble Learning

1.
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031

摘要

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.
- 自动图像标注 /
- 机器学习 /
- 集成分类器 /
- 快速随机森林算法
Abstract: 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.
- Automatic image annotation /
- machine learning /
- ensemble learning /
- fast random forest (FRF)

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参考文献(1)

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