面向高维图像特征匹配的多次随机子向量量化哈希算法  

Multiple Randomized Sub-vectors Quantization Hashing for High-Dimensional Image Feature Matching

作　　者：杨恒[1] 王庆[1] 何周灿[1] 

机构地区：[1]西北工业大学计算机学院,西安710072

出　　处：《计算机辅助设计与图形学学报》2010年第3期494-502,510,共10页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(60873085);国家"八六三"高技术研究发展计划(2007AA01Z314);"新世纪优秀人才"计划(NCET-06-0882)

摘　　要：为了解决高维图像特征的高效匹配问题,提出一种新的基于多次随机子向量量化哈希(MRSVQH)的索引算法.该算法根据随机选择的若干子向量的L2范数对特征向量进行量化,并根据量化值对特征向量进行散列,构建出哈希索引结构;为了提高搜索精度,类似的哈希索引结构被多次构建.搜索时仅考察与查询向量有相同哈希值的特征向量集合,缩减了搜索范围.实验数据表明,与经典的BBF和LSH算法相比,MRSVQH算法在图像特征的最近邻搜索精度和搜索速度方面都有较大的性能提升,在图像匹配和图像检索的应用中具有优势.This paper proposes a new indexing algorithm based on multiple randomized sub-vectors quantization hashing （MRSVQH） for efficient high-dimensional image feature matching. The proposed MRSVQH algorithm quantizes feature vectors according to the L2 norms of the randomized sub-vectors and hashes feature vectors to their corresponding hash buckets. Such index structures are built for multiple times in order to increase the searching accuracy. On the query stage, the searching process is limited only in the feature vectors that have the same hash value to the query one. Experimental results demonstrate that our MRSVQH algorithm can significantly improve the performance of nearest neighbor searching of image features in both accuracy and efficiency compared to the classic BBF and LSH algorithms, which in turn makes it particularly suitable for image matching and image retrieval

关 键 词：高维特征匹配 最近邻搜索 图像匹配 图像检索 多次随机子向量量化哈希 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]