摘要
该文提出基于Bag of words模型,提取图像的SIFT特征,然后用K-medoids算法对其进行聚类,生成词典查询所需用的关键字,最后用adaboosting算法构建分类器,实验采用pascal图像库中的数据进行训练和测试,实验证明,该算法具有训练和测试速度快,分类精度高等特点,特征提取速度和分类速度非常快。
Image classification has a wide application in computer vision and image processing area,and it also has significant value in practi cal applications.Therefore,this paper proposes a model based on Bag of words to extract the SIFT characteristic of image,and then using K-medoids algorithm to cluster them and generate the Key words dictionary query needs.Finally,it builds the sorter with adaboosting al gorithm.The experiment uses the data in pascal image libary to do training and testing,and the result shows that this algorithm has high training and testing speed,high accuracy on classification,ect.Moreover,the feature extraction and classificiton speed is very high.
出处
《电脑知识与技术》
2012年第3X期2075-2076,共2页
Computer Knowledge and Technology
基金
河南省创新型科技人才队伍建设工程项目(094200510009)
关键词
尺度不变特征
聚类算法
图像分类性能
scale invariant features
clustering algorithm
image classification performance
