A.W.M. Smeulders and R. Jain (eds). Proc. 1st Workshop on Image Databases and Multi-Media Search, 1996.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....by the Fund for the Promotion of Research at the Technion. Email: rabani cs.technion.ac.il. 1 Introduction Problem definition and motivation. This paper is concerned with nearest neighbor search (NNS) a fundamental problem in computational geometry, with applications to a variety of areas [15, 20, 17, 33, 16, 22, 14, 32, 19, 23, 34, 8, 21]. The problem is defined as follows: In some vector space endowed with a distance function (typically a d dimensional Euclidean space) we are given a set of n points (called the database) Given any other point (called a query) we must find the closest point to it in the database. We have to ....

A.W.M. Smeulders and R. Jain (eds). Proc. 1st Workshop on Image Databases and MultiMedia Search, 1996.

.... can define this nearest neighbor search (NNS) problem in any vector space, and with any metric (or even with a non metric distance function) Recently, theoretical research into this problem gained some momentum, inspired in part by applications to multimedia information retrieval and data mining [44, 17, 42, 23, 29, 45, 7, 22]. Trivially, one solution to the problem is to store the raw data, and in response to a query, to compute the distance from the query to each of the n points. Typically, as in the Euclidean case, this would take O(nd) storage and O(nd) search time. On the other extreme, if the set of possible ....

A.W.M. Smeulders and R. Jain (eds). Proc. 1st Workshop on Image Databases and Multi-Media Search, 1996.

....Fund for the Promotion of Research at the Technion. Email: rabani cs.technion.ac.il 1 Introduction Motivation. Searching for a nearest neighbor among a specified database of points is a fundamental computational task that arises in a variety of application areas, including information retrieval [31, 32], data mining [19] pattern recognition [8, 14] machine learning [7] computer vision [4] data compression [17] and statistical data analysis [10] In many of these applications the database points are represented as vectors in some high dimensional space. For example, latent semantic indexing ....

A.W.M. Smeulders and R. Jain (eds). Proc. 1st Workshop on Image Databases and MultiMedia Search, 1996.

.... can define this nearest neighbor search (NNS) problem in any vector space, and with any metric (or even with a non metric distance function) Recently, theoretical research into this problem gained some momentum, inspired in part by applications to multimedia information retrieval and data mining [42, 16, 40, 22, 27, 43, 6, 21]. Trivially, one solution to the problem is to store the raw data, and in response to a query, to compute the distance from the query to each of the n points. Typically, as in the Euclidean case, this would take O(nd) storage and O(nd) search time. On the other extreme, if the set of possible ....

A.W.M. Smeulders and R. Jain (eds). Proc. 1st Workshop on Image Databases and Multi-Media Search, 1996.

No context found.

A.W.M. Smeulders and R. Jain (eds). Proc. 1st Workshop on Image Databases and Multi-Media Search, 1996.

No context found.

A.W.M. Smeulders and R. Jain (eds). Proc. 1st Workshop on Image Databases and MultiMedia Search, 1996.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC