Metric databases are databases where a metric distance function is defined for pairs of database objects. In such databases, similarity queries in the form of range queries or k-nearest neighbor queries are the most important query types. In traditional query processing, single queries are issued

support for content-based queries over image databases. Content-based retrieval techniques have been extensively studied for textual documents in the area of automatic information retrieval [24, 2]. This paper describes how these techniques can be adapted for ranked retrieval over image databases. Speci

study the processing of similarity nearest neighbor queries in large distributed multidimensional databases, where objects are represented as vectors in a vector space, and are distributed in a multi-computer environment. The departure from the centralized case embodies a number of advantages

to similarity search problem become cost-inefficient due to the so-called dimensionality curse. This phenomenon has been observed and shown that in high-dimensional data spaces the probability of overlaps between a query and data regions in a multidimensional access method (MAM) is very high. Hence

Query-by-content image database will be based on similarity, rater than on matching, where similarity is a measure that is defined and meaningful for every pair of images in the image space. Since it is the human user that, in the end, has to be satisfied with the results of the query

derived from fuzzy logic in that it uses the equivalent in the similarity domain of the and, or, and not operations. The approach is much more general that that, however, and can be adapted to work with any operation that combines together similarity judgment. With this approach, a query can

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We review the major paradigms for similarity queries, in particular those that allow approximate results. We propose an original classification schema which easily allows existing approaches to be compared along several independent coordinates, such as quality of results, error metrics, and user

for effective indexing methods for angular similarity queries. We first discuss how to efficiently process such queries and propose effective access structures suited to angular similarity measures. In particular, we propose two classes of access structures, namely, Angular-sweep and Cone-shell, which perform

Abstract. In this paper an effective context-based approach for interactive similarity queries is presented. By exploiting the notion of image “context”, it is possible to associate different meanings to the same query image. This is indeed necessary to model complex query concepts that, due