Humpback whale’s flukes segmentation algorithms

Andrea Castro Cabanillas, Victor H. Ayma

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

1 Scopus citations


Photo-identification consists of the analysis of photographs to identify cetacean individuals based on unique characteristics that each specimen of the same species exhibits. The use of this tool allows us to carry out studies about the size of its population and migratory routes by comparing catalogues. However, the number of images that make up these catalogues is large, so the manual execution of photo-identification takes considerable time. On the other hand, many of the methods proposed for the automation of this task coincide in proposing a segmentation phase to ensure that the identification algorithm takes into account only the characteristics of the cetacean and not the background. Thus, in this work, we compared four segmentation techniques from the image processing and computer vision fields to isolate whales’ flukes. We evaluated the Otsu (OTSU), Chan Vese (CV), Fully Convolutional Networks (FCN), and Pyramid Scene Parsing Network (PSP) algorithms in a subset of images from the Humpback Whale Identification Challenge dataset. The experimental results show that the FCN and PSP algorithms performed similarly and were superior to the OTSU and CV segmentation techniques.

Original languageEnglish
Title of host publicationInformation Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings
EditorsJuan Antonio Lossio-Ventura, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)978-3-030-76227-8
StatePublished - 2021
Externally publishedYes
Event7th Annual International Conference on Information Management and Big Data, SIMBig 2020 - Virtual, Online
Duration: 1 Oct 20203 Oct 2020

Publication series

NameCommunications in Computer and Information Science
Volume1410 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference7th Annual International Conference on Information Management and Big Data, SIMBig 2020
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • Artificial intelligence
  • Cetology
  • Computer vision
  • Image segmentation
  • Photo-identification


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