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

  1. Eduardo Fidalgo Fernández 1
  2. Enrique Alegre Gutiérrez 1
  3. Laura Fernández Robles 1
  4. Víctor González Castro 1
  1. 1 Universidad de León

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Year of publication: 2019

Volume: 16

Issue: 3

Pages: 358-368

Type: Article

DOI: 10.4995/RIAI.2019.10640 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista iberoamericana de automática e informática industrial ( RIAI )


Cited by

  • Scopus Cited by: 6 (28-02-2023)
  • Web of Science Cited by: 6 (20-03-2023)

JCR (Journal Impact Factor)

  • Year 2019
  • Journal Impact Factor: 1.036
  • Journal Impact Factor without self cites: 0.786
  • Article influence score: 0.11
  • Best Quartile: Q4
  • Area: AUTOMATION & CONTROL SYSTEMS Quartile: Q4 Rank in area: 51/63 (Ranking edition: SCIE)
  • Area: ROBOTICS Quartile: Q4 Rank in area: 27/28 (Ranking edition: SCIE)

SCImago Journal Rank

  • Year 2019
  • SJR Journal Impact: 0.376
  • Best Quartile: Q2
  • Area: Computer Science (miscellaneous) Quartile: Q2 Rank in area: 112/466
  • Area: Control and Systems Engineering Quartile: Q2 Rank in area: 157/783

Scopus CiteScore

  • Year 2019
  • CiteScore of the Journal : 2.2
  • Area: Computer Science (all) Percentile: 62
  • Area: Control and Systems Engineering Percentile: 46

Journal Citation Indicator (JCI)

  • Year 2019
  • Journal Citation Indicator (JCI): 0.37
  • Best Quartile: Q3
  • Area: AUTOMATION & CONTROL SYSTEMS Quartile: Q3 Rank in area: 55/76
  • Area: ROBOTICS Quartile: Q4 Rank in area: 31/38


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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