A novel approach for image feature extraction using HSV model color and niters wavelets

Cristian José Lopez Del Alamo, Lizeth Joseline Fuentes Pérez, Luciano Arnaldo Romero Calla, Wilber Roberto Ramos Lovón

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Due to the advancement of computing and the power of the new hardware, more economical, it is now feasible to have thousands of images which can be analyzed to allow classification for its shape and/or color. Furthermore, techniques and efficiency of the classification depends on the characteristics to be obtained of images in order to compare and classify them according to their similarity. Some images, such as model cars, planes and boats, can be discriminated by their shape. However, other images such as butterfly species where the shape is similar, the color plays an important role in the discrimination task. In this research we propose a novel approach to extract distinctive features of images by combining the HSV color model and wavelets filters. Furthermore, we investigate the best combination of features color and form. Experiments have shown improved performance by combining the HSV color model with Gabor wavelets.

Original languageEnglish
Title of host publicationProceedings of the 2013 39th Latin American Computing Conference, CLEI 2013
DOIs
StatePublished - 2013
Event2013 39th Latin American Computing Conference, CLEI 2013 - Naiguata, Vargas, Venezuela, Bolivarian Republic of
Duration: 7 Oct 201311 Oct 2013

Publication series

NameProceedings of the 2013 39th Latin American Computing Conference, CLEI 2013

Conference

Conference2013 39th Latin American Computing Conference, CLEI 2013
Country/TerritoryVenezuela, Bolivarian Republic of
CityNaiguata, Vargas
Period7/10/1311/10/13

Keywords

  • Gabor wavelets Haar wavelets
  • HSV
  • image classification
  • image recognition

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