Evaluating tradeoff between recall and perfomance of GPU permutation index
Query-by-content, by means of similarity search, is a fundamental operation for applications that deal with multimedia data. For this kind of query it is meaningless to look for elements exactly equal to a given one as query. Instead, we need to measure the dissimilarity between the query object and...
Guardado en:
| Autores principales: | , , , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2013
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/31737 |
| Aporte de: |
| Sumario: | Query-by-content, by means of similarity search, is a fundamental operation for applications that deal with multimedia data. For this kind of query it is meaningless to look for elements exactly equal to a given one as query. Instead, we need to measure the dissimilarity between the query object and each database object. This search problem can be formalized with the concept of metric space. In this scenario, the search efficiency is understood as minimizing the number of distance calculations required to answer them. Building an index can be a solution, but with very large metric databases is not enough, it is also necessary to speed up the queries by using high performance computing, as GPU, and in some cases is reasonable to accept a fast answer although it was inexact. In this work we evaluate the tradeoff between the answer quality and time performance of our implementation of Permutation Index, on a pure GPU architecture, used to solve in parallel multiple approximate similarity searches on metric databases. |
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