MB⁺-Tree: An Index Structure for Content-Based Retrieval

Abstract

Though standard database management systems(DBMSs) dealing mainly with alphanumeric or spatial data have reached a high level of maturity, the techniques employed there cannot be effectively applied to the management of other multimedia entities such as images and video, primarily because of the differing nature of the data and the varying types of the queries posed to the system. Unlike traditional DBMSs, which normally retrieve a few records through the specification of exact queries based on the notion of “equality,” the types of queries expected in an image/video DBMS are relatively vague or fuzzy and are based on the notion of “similarity”. The result is that the similarity measure used can vary depending on the query posed to the system. Thus the indexing structure should be able to satisfy similarity-based queries for a wide range of similarity measures. Also, a realistic expectation of an image/video DBMS would be for it to reduce the search space by eliminating records which are completely irrelevant to the query. This “browsing” or “filtering” approach to query processing is suited to an image/video DBMS since the human visual system is quite capable of rapidly browsing through hundreds of images. In addition, the querying process in image/video DBMSs is expected to be iterative with progressively more refined queries being issued during the later stages. Thus the indexing structure should be able to efficiently support both vague queries(retrieving a large number of approximate matches) and “non-vague” queries(retrieving a small number of close matches).