Relevance feedback through the generation of trees for image retrieval based on multitexton histogram

Yuber Elmer Velazco Paredes, Roxana Flores Quispe, Raquel Patino-Escarcina, Cesar Beltran-Castanon

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

The Content-based image retrieval (CBIR) systems and their application in different areas of development, are current research topics, however the semantic gap between low-level image features and high-level semantic concepts handled by the user, is one of the main problems in the image retrieval. On the other hand, the relevance feedback has been used on many CBIR systems such as an effective solution to reduce the semantic gap. For that reason this paper proposes a method of relevance feedback based on the generation of trees and Histogram Multitexton descriptor. This method has been compared with the conventional RF algorithms 'Query vector modification', and show significant improvements in terms of effectiveness in the image retrieval. Also the dimensionality of the Histogram Multitexton descriptor has been tested and with the first 64 dimensions increase its effectiveness which permit to reduce the computational processing time.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2011 30th International Conference of the Chilean Computer Science Society, SCCC 2011
Páginas1-7
Número de páginas7
DOI
EstadoPublicada - 2012
Evento2011 30th International Conference of the Chilean Computer Science Society, SCCC 2011 - Curico, Chile
Duración: 9 nov. 201111 nov. 2011

Serie de la publicación

NombreProceedings - International Conference of the Chilean Computer Science Society, SCCC
ISSN (versión impresa)1522-4902

Conferencia

Conferencia2011 30th International Conference of the Chilean Computer Science Society, SCCC 2011
País/TerritorioChile
CiudadCurico
Período9/11/1111/11/11

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