Similarity search is a very important operation in multimedia databases and other database applications involving complex objects, and involves finding objects in a data set S similar to a query object q, based on some similarity measure. In this article, we focus on methods for similarity search that make the general assumption that similarity is represented with a distance metric d. Existing methods for handling similarity search in this setting typically fall into one of two classes. The first directly indexes the objects based on distances (distance-based indexing), while the second is based on mapping to a vector space (mapping-based approach). The main part of this article is dedicated to a survey of distance-based indexing methods, but we also briefly outline how search occurs in mapping-based methods. We also present a general framework for performing search based on distances, and present algorithms for common types of queries that operate on an arbitrary “search hierarchy. ” These algorithms can be applied on each of the methods presented, provided a suitable search hierarchy is defined.

Similarity search is a very important operation in multimedia databases and other database applications involving complex objects, and involves finding objects in a data set S similar to a query object q, based on some distance measure d, usually a distance metric. Existing methods for handling similarity search in this setting fall into one of two classes. The first is based on mapping to a low-dimensionalvector space (making use of data structures such as the R-tree), while the second directly indexes the objects based on distances (making use of data structures such as the M-tree). We introduce a general framework for performing search based on distances, and present an incremental nearest neighbor algorithm that operates on an arbitrary "search hierarchy". We show how this framework can be applied in both classes of similarity search methods, by defining a suitable search hierarchy for a number of different indexing structures. Armed with an appropriate search hierarchy, our algorithm thus performs incremental similarity search, wherein the result objects are reported one by one in order of similarity to a query object, with as little effort as possible expended to produce each new result object. This is especially important in interactive database applications, as it makes it possible to display partial query results early. The incremental aspect also provides significant benefits in situations when the number of desired neighbors is unknown in advance. Furthermore, our algorithm is at least as efficient as existing k-nearest neighbor algorithms, in terms of the number of distance computations and index node accesses. In fact, provided that the search hierarchy is properly defined, our algorithm can be shown to be optimal in the sense of performing as few distance ...

Increasing application demands are pushing database management systems (DBMSs) towards providing adequate and efficient support for content-based retrieval over multimedia objects (e.g., images, video, audio, time-series, spatial and spatio-temporal data). Recently, several powerful models for multimedia similarity retrieval have been proposed. An important aspect of these models is the notion of query refinement: a technique that allows the users to interactively specify their information need to the system by providing relevance ranking on example objects. Query refinement has several motivations. First, the `starting' query may only partially capture the user's information need. The user may find better examples among the answers returned to the starting query which then become the basis of the `refined' query. Second, multimedia objects are represented as a collection of features. The relative importance of these features in computing the similarity between objects (inter-feature w...

Unlike traditional database management systems, in multimedia databases that support content-based retrieval over multimedia objects, it is difficult for users to express their exact information need directly in the form of a precise query. A typical interface supported by content-based retrieval systems allows users to express their query in the form of examples of objects similar to the ones they wish to retrieve. Such a user interface, however, requires mechanisms to learn the query representation from the examples provided by the user. In our previous work, we proposed a query refinement mechanism in which a query representation is modified by adding new relevant examples based on user feedback. In this paper, we describe query processing mechanisms that can efficiently support query expansion using multidimensional index structures.

With the expansion of the Internet, searching for information goes beyond the boundary of physical libraries. Millions of documents of various media types, suchas text, image, video, audio, graphics, and animation, are available around the world and linked by the Internet. Unfortunately, the state of the art of search engines for media types other than text lags far behind their text counterparts. To address this situation, wehave developed the Multimedia Analysis and Retrieval System #MARS#. This paper reports some of the progress made over the years towards exploring information retrieval beyond the text domain. In particular, the following aspects of MARS are addressed in the paper: visual feature extraction, retrieval models, query reformulation techniques, e#cient execution speed performance and user interface considerations. Extensive experimental results are reported to validate the proposed approaches. 1 Introduction Huge amounts of digital data are being generated...

An overview is presented of object-based and image-based representations of objects by their interiors. The representations are distinguished by the manner in which they can be used to answer two fundamental queries in database applications: (1) Feature query: given an object, determine its constituent cells (i.e., their locations in space). (2) Location query: given a cell (i.e., a location in space), determine the identity of the object (or objects) of which it is a member as well as the remaining constituent cells of the object (or objects). Regardless of the representation that is used, the generation of responses to the feature and location queries is facilitated by building an index (i.e., the result of a sort) either on the objects or on their locations in space, and implementing it using an access structure that correlates the objects with the locations. Assuming the presence of an access structure, implicit (i.e., image-based) representations are described that are good for finding the objects associated with a particular location or cell (i.e., the location query), while requiring that all cells be examined when determining the locations associated with a particular object (i.e., the feature query). In contrast, explicit (i.e., object-based) representations are good for the feature query,

The importance of multidimensional index structures to numerous emerging database applications is well established. However, before these index structures can be supported as access methods (AMs) in a "commercial-strength" database management system (DBMS), efficient techniques to provide transactional access to data via the index structure must be developed. Concurrent accesses to data via index structures introduce the problem of protecting ranges specified in the retrieval from phantom insertions and deletions (the phantom problem). This paper presents a dynamic granular locking approach to phantom protection in Generalized Search Trees (GiSTs), an index structure supporting an extensible set of queries and data types. The granular locking technique offers a high degree of concurrency and has a low lock overhead. Our experiments show that the granular locking technique (1) scales well under various system loads and (2) similar to the B-tree case, provides a significantly more effici...

With advances in the computer technologies and the advent of the World Wide Web, there has been an explosion in the amount and complexity of digital data being generated, stored, transmitted, analyzed, and accessed. Much of this information is multimedia in nature, including digital images, video, audio, graphics, and text data. In order to make use of this vast amount of data, efficient and effectivetechniques to analyze and retrievemultimedia information based on its content need to be developed.