Controlled condensation in K-NN and its application for real time color identification
Guardado en:
| Autores: | , |
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| Formato: | artículo original |
| Estado: | Versión publicada |
| Fecha de Publicación: | 2017 |
| Descripción: | k-NN algorithms are frequently used in statistical classification. They are accurate and distribution free. Despite these advantages, k-NN algorithms imply a high computational cost. To find efficient ways to implement them is an important challenge in pattern recognition. In this article, an improved version of the k-NN Controlled Condensation algorithm is introduced. Its potential for instantaneous color identification in real time is also analyzed. This algorithm is based on the representation of data in terms of a reduced set of informative prototypes. It includes two parameters to control the balance between speed and precision. This gives us the opportunity to achieve a convenient percentage of condensation without incurring in an important loss of accuracy. We test our proposal in an instantaneous color identification exercise in video images. We achieve the real time identification by using k-NN Controlled Condensation executed through multi-threading programming methods. The results are encouraging. |
| País: | Portal de Revistas UCR |
| Institución: | Universidad de Costa Rica |
| Repositorio: | Portal de Revistas UCR |
| Lenguaje: | Español |
| OAI Identifier: | oai:portal.ucr.ac.cr:article/22354 |
| Acceso en línea: | https://revistas.ucr.ac.cr/index.php/matematica/article/view/22354 |
| Palabra clave: | supervised classification nearest neighbours multi-threading condensation prototype selection clasificación supervisada vecinos cercanos programación multihilos condensación selección de prototipos |