逐维聚类的相似度索引算法
A Similarity Indexing Algorithm Based on Each Dimension Clustering
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摘要: 随着多媒体信息技术的迅速发展 ,多维度索引技术在图像、视频等可视信息的存储、检索方面成为一个重要的研究领域 针对“维数危机”难题 ,提出逐维聚类相似度索引算法 该算法根据数据集的分布特性 ,对特征矢量的每一维进行聚类 算法在实现检索时可以逐步滤除与查询矢量不相似的数据集 ,缩小检索范围 ,进而提高了检索速度 实验结果表明 ,逐维聚类算法适用于基于相似度的高维数据矢量检索和查询 ,是一种简单、灵活的索引结构Abstract: With the rapid development of multimedia information technology, multi dimensional indexing technique becomes an important research area with respect to the store and retrieval of visual information such as video and image To overcome the prominent ‘dimension curse’ problem, a novel method named similarity indexing algorithm is put forward based on each dimension clustering The method is used to realize each dimension clustering of the feature vector according to data distribution The process of realization can filter the irrelative data and reduce the search range step by step, hence speeding up the retrieval Experimental results show that the proposed method is suitable to perform the retrieval and search based on similarity indexing for the data with high dimension And it proves to be a simple and flexible indexing structure
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