A pivot-based index structure for combination of feature vectors
A pivot-based index structure for combination of feature vectors
复制标题
用于特征向量组合的基于枢轴的索引结构
DOI：
10.1145/1066677.1066945
复制
发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Tobias Schreck
中科院分区：
文献类型：
--
作者：
B. Bustos;D. Keim;Tobias Schreck
我们提出了一种新的索引模式，提供了高效的最近邻查询多媒体数据库中的对象描述的多个特征向量。同时使用几个（静态或动态）加权的特征向量的检索效果方面的好处，以前已经证明。支持高效的多特征向量相似性查询是一个开放的问题，因为现有的索引方法不支持动态参数化的距离函数。我们提出了一个解决方案，这个问题依赖于几个基于索引的度量指标的组合。我们定义的索引结构，这些结构上执行最近邻查询的算法，并证明了两个真实世界的图像数据库进行实验的可行性。实验结果表明，现有的访问方法的性能显着改善。
We present a novel indexing schema that provides efficient nearest-neighbor queries in multimedia databases consisting of objects described by multiple feature vectors. The benefits of the simultaneous usage of several (statically or dynamically) weighted feature vectors with respect to retrieval effectiveness have been previously demonstrated. Support for efficient multi-feature vector similarity queries is an open problem, as existing indexing methods do not support dynamically parameterized distance functions. We present a solution for this problem relying on a combination of several pivot-based metric indices. We define the index structure, present algorithms for performing nearest-neighbor queries on these structures, and demonstrate the feasibility by experiments conducted on two real-world image databases. The experimental results show a significant performance improvement over existing access methods.