Pattern Analysis and Applications

, Volume 18, Issue 4, pp 783–797 | Cite as

Evaluating cluster detection algorithms and feature extraction techniques in automatic classification of fish species

  • Marco T. A. Rodrigues
  • Mário H. G. Freitas
  • Flávio L. C. Pádua
  • Rogério M. Gomes
  • Eduardo G. Carrano
Theoretical advances


This paper proposes five different schemes for automatic classification of fish species. These schemes make the species recognition based on image sample analysis. Different techniques have been combined for building the classifiers: three feature extraction techniques (PCA, SIFT and SIFT + VLAD + PCA), three data clustering algorithms (aiNet, ARIA and k-means) and three input classifiers (k-NN, SIFT class. and k-means class) are tested. When compared to common methodologies, which are based on human observation, it is believed that these schemes are able to provide significant improvement in time and financial resources spent in classification. Two datasets have been considered: (1) a dataset with image samples of six fish species which are perfectly conserved in formaldehyde solution, and; (2) a dataset composed of images of four fish species in real-world conditions (in vivo). The five proposed schemes have been evaluated in both datasets, and a ranking for the methods has been derived for each one.


Fish automatic classification Feature extraction Image clustering 



The authors thank the support of FAPEMIG-Brazil under Procs. EDT-162/07 and APQ-01180-10, CEFET-MG under Proc. No 023-076/09, CNPq-Brazil and of CAPES-Brazil.


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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Marco T. A. Rodrigues
    • 1
  • Mário H. G. Freitas
    • 1
  • Flávio L. C. Pádua
    • 1
  • Rogério M. Gomes
    • 1
  • Eduardo G. Carrano
    • 2
  1. 1.Department of ComputingCentro Federal de Educação Tecnológica de Minas GeraisBelo HorizonteBrazil
  2. 2.Department of Electrical EngineeringUniversidade Federal de Minas GeraisBelo HorizonteBrazil

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