基于RSOM树的图像K近邻求解算法  被引量:2

K nearest neighbors detecting algorithm based on a RSOM tree

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作  者:郑君君[1] 夏胜平[1] 李新光[1] 祝一薇[1] 刘建军[1] 谭立球[1,2] 

机构地区:[1]国防科技大学电子科学与工程学院ATR实验室,湖南长沙410073 [2]中南大学现代教育中心网络工程研究所,湖南长沙410075

出  处:《山东大学学报(工学版)》2011年第2期80-84,共5页Journal of Shandong University(Engineering Science)

基  金:国家自然科学基金资助项目(60972114)

摘  要:提出了一种基于RSOM(recursive self-organizing mapping,RSOM)树、利用SIFT(scale invariant feature trans-form)特征为索引的海量图像集中K近邻的求解方案。对图像编号并提取SIFT特征,依据SIFT特征将图像的编号存储至RSOM树的叶节点中;搜索时用匹配的SIFT特征个数作为指标获得K近邻图像的候选集,用迭代Pro-crustes方法几何约束得到精确求解结果。利用5万余幅图像数据进行实验测试,结果证实了该方法的有效性。A K nearest neighbors detecting algorithm based on a RSOM(recursive self-organizing mapping) clustering tree was proposed by using the scale invariant feature transform(SIFT) feature as the indices.Images were labeled and SIFT features were extracted and the number of the images were stored in the leaf node of the RSOM clustering tree.Using matched feature number as the criterion of the candidate set of K nearest-neighbors set,the iterative Procrustes method was employed to obtain more precise results.More than 50 000 images were tested and the experimental results showed the high efficiency of the proposal method.

关 键 词:图像内容检索 RSOM K最近邻 聚类树 SIFT 快速算法 局部不变特征 

分 类 号:TP391[自动化与计算机技术—计算机应用技术]

 

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