Fusión temprana de descriptores extraídos de mapas de prominencia multi-nivel para clasificar imágenes

  1. Fidalgo, E. 1
  2. Alegre, E. 1
  3. Fernández-Robles, L. 1
  4. González-Castro, V. 1
  1. 1 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

Revista:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Año de publicación: 2019

Volumen: 16

Número: 3

Páginas: 358-368

Tipo: Artículo

DOI: 10.4995/RIAI.2019.10640 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Revista iberoamericana de automática e informática industrial ( RIAI )

Resumen

In this paper, we propose a method that improves the classification of images. Considering saliency maps as if they were topographic maps and filtering the characteristics of the image’s background, the Bag of VisualWords (BoVW) coding is improved. First, we evaluated six known algorithms to generate saliency maps and we selected GBVS and SIM because they are the ones that retain most of the information of the object. Next, we eliminated the extracted SIFT descriptors belonging to the background by filtering features based on binary images obtained at various levels of the selected saliency maps. We filtered the descriptors by obtaining layers at various levels of the saliency maps, and we evaluated the early fusion of the SIFT descriptors contained in these layers into five dierent datasets. The results obtained indicate that the proposed method always improves the reference method when combining the first two layers of GBVS or SIM and the dataset contains images with a single object.

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