基于协同传递机制的形状匹配算法  

SHAPE MATCHING ALGORITHM BASED ON COOPERATIVE TRANSMISSION MECHANISM

作　　者：王江辉 吴小俊[1] Wang Jianghui;Wu Xiaojun(School of IoT Engineering,Jiangnan University,Wuxi 214122,Jiangsu,China)

机构地区：[1]江南大学物联网工程学院,江苏无锡214122

出　　处：《计算机应用与软件》2018年第4期215-219,共5页Computer Applications and Software

基　　金：国家自然科学基金项目(61373055;61672265);江苏省教育厅科技成果产业化推进项目(JH10-28);江苏省产学研创新项目(BY2012059)

摘　　要：轮廓点分布直方图CPDH(Contours Points Distribution Histogram)是一种形状描述子,但它对微小形变比较敏感且在大数据集下的检索效果不佳。提出基于协同传递机制的半监督学习框架Co-transduction与CPDH相结合的算法。通过给定CPDH的相似度度量和另一种描述符的度量结果,对一幅查询图像,利用其中一种度量准则迭代检索出与查询图像最相似的目标形状将其标记。用另一种相似性度量重新检索并排序已标记的形状,反之亦然。该改进算法较原始CPDH在大数据集下(MPEG-7)的检索性能更优,检索精确率达到86%,比原算法提高约10%。Contour points distribution histogram(CPDH)is a shape descriptor,but it is sensitive to small deformations and under the big data set under the retrieval effect is not good.A Semi-supervised learning framework based on cooperative transmission mechanism co-transduction combined with CPDH algorithm was proposed.Given the similarity measure of CPDH and the measurement result of another descriptor,for a query image,the most similar target shape to the query image was retrieved iteratively using one of the metric criteria and labelled.Then another similarity measure was used to retrieve and sorted the marked shapes,and vice versa.Compared with the original CPDH,this improved algorithm has better retrieval performance under the big data set(MPEG-7),and the retrieval accuracy is 86%,which is about 10%higher than the original algorithm.

关 键 词：形状匹配 协同传递 半监督学习 迭代检索 轮廓点分布直方图 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]