SciELO - Scientific Electronic Library Online

vol.23 issue1SC: a novel fuzzy criterion for solving engineering and constrained optimization problemsCombining neural networks andgeostatistics for landslide hazardassessment of San Salvador metropolitan area, El Salvador author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand




Related links

  • Have no similar articlesSimilars in SciELO


Revista de Matemática Teoría y Aplicaciones

Print version ISSN 1409-2433


VILLAR-PATINO, Carmen  and  CUEVAS-COVARRUBIAS, Carlos. Controlled condensation in k-NN and its application for real time color identification. Rev. Mat [online]. 2016, vol.23, n.1, pp.143-154. ISSN 1409-2433.

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.

Keywords : supervised classification; nearest neighbours; multi-threading; condensation; prototype selection.

        · abstract in Spanish     · text in Spanish     · Spanish ( pdf )