摘要
为解决藻类图像分类检索的问题,提出了一套基于内容的方法对藻类图像进行分类检索。针对海洋生物图像对颜色特征不敏感的特点,采用SIFT(Scale-Invariant Feature Transform)算法针对藻类图像的形状特征进行提取。使用PCA(Principal Component Analysis)技术对特征进行降维,有效避免了维数灾难。采用K-means算法进行聚类,简单高效。用词包对聚类结果进行打包,以便后续的识别。最后用KNN(K-Nearest Neighbors)算法进行识别检索。实验结果与事实吻合,为藻类造成的环境污染问题的研究提供了有效的技术支持。
The water damage problem is more and more worse in recent years,so the problem of algae image classification and retrieval is extremely urgent. To solve this problem the algorithm based on content is proposed. SIFT（ Scale-invariant feature transform） algorithm is used to extract shape feature of algae image because marine biological image is color insensitive. The dimension of the feature vector generated by SIFT can reach up to 128. The higher dimensions can affect classification prediction and can lead to high computational complexity. PCA（ Principal Component Analysis） technology is adopted to reduce the dimension of the feature avoiding dimension disaster. K-means is employed to clustering algorithm,which is simple and effective. The result of the clustering algorithm is packaged by Bag of Words in order to subsequent identification. KNN（ K-Nearest Neighbors） algorithm is used to recognition. The experimental results are finally consistent with the fact. It provides effective support for the research of environmental problems which is caused by algae.
出处
《吉林大学学报（信息科学版）》
CAS
2015年第5期538-543,共6页
Journal of Jilin University（Information Science Edition）
基金
国家自然科学基金资助项目(60905022)
山东省教育科学"十二五"规划2013年度重点课题基金资助项目(2013GZ042)
山东青年政治学院2015-2016年度科研计划基金资助项目(2015QN10)
山东青年政治学院2014年度教学改革研究基金资助项目(201417)
关键词
图像分类
图像检索
藻类图像
SIFT特征
PCA降维
image classification
image retrieval
algae image
scale-invariant feature transform（SIFT）
principal component analysis（PCA）
