一种支持快速相似检索的多维索引结构

基于内容的图像检索是一种典型的相似检索问题,对于尺度空间上的图像相似匹配问题,一般认为距离计算费用很高.因此,需要建立有效的索引结构,以减少每个查询中的距离计算次数.为此,基于数据空间的"优化划分",并且使用"代表点",以层次结构方式划分数据,提出了一种新的基于距离的相似索引结构opt-树及其变种(-树.为了更有效地支持基于内容的图像检索,在(-树索引结构中采用了"(-最优化划分"和"(-对称冗余存储"策略,以提高相似检索的效率.详细讨论了这种索引结构的建立与检索等问题,并给出了相应的算法.实验结果显示了这种索引技术的有效性.

A Multidimensional Index Structure for Fast Similarity Retrieval

A typical example of similarity search is to find the images similar to a given image in a large collection of images. This paper focuses on the important and technically difficult case where each data element is represented by a point in a large metric space. As distance function employed is metric and distance calculations are assumed to be computationally expensive, it is necessary to index data objects in the metric space such that less distance evaluations are performed to support fast similarity queries. Based on the optimal partition method that uses representative points to partition the data space into subsets in a hierarchical manner, a novel distance-based index structure opt-tree and its variant h-tree are proposed. In order to fully support the content-based image retrieval, the optimal strategies for the partition of data space and data redundancy storage, which are called h-optimal partitioning and h-symmetric redundancy storage respectively, are adopted in the h-tree index structure to achieve the high performance of the similarity retrievals. In this paper, the decisions and the algorithms which led to opt-tree and its variant h-tree are discussed in detail, and the experimental results show that this index structure is effective.