| Journal: | Computación y sistemas |
| Database: | |
| System number: | 000560669 |
| ISSN: | 1405-5546 |
| Authors: | Figueroa, Karina1 Camarena Ibarrola, Antonio2 Valero, Luis1 |
| Institutions: | 1Universidad Michoacana de San Nicolás Hidalgo, Facultad de Ciencias Físico Matemáticas, México 2Universidad Michoacana de San Nicolás Hidalgo, Facultad de Ingeniería Eléctrica, México |
| Year: | 2022 |
| Season: | Ene-Mar |
| Volumen: | 26 |
| Number: | 1 |
| Pages: | 71-79 |
| Country: | México |
| Language: | Inglés |
| English abstract | Similarity searching is the most important task in multimedia databases, It consists in retrieving the most similar elements to a given query from a database, knowing that an element identical to the query would not be found. Dissimilarity between objects is measured with a distance function (usually expensive to compute), this allows approaching this problem with a metric space. Many algorithms have been designed to address this problem, in particular, the Permutation Based index has shown an unbeatable performance. This technique uses reference objects to determine a string for each element in the database that is a permutation of the same string. However, Huge databases and the memory required for these indexes make this problem a real challenge. In this paper, we present an improvement to the first approach where classes of reference objects were used instead of single references. In this paper, a new way to choose these classes is proposed and a new way to evaluate similarity between permutations. Our experiments show that we can avoid distance evaluations up to 90% with respect to the original technique, and up to 80% to the first approach. |
| Keyword: | Similarity searching, Metric spaces, Pattern recognition, Nearest neighbor |
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