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Automated otolith image classification with multiple views: an evaluation on Sciaenidae.

J Y Wong1, C Chu1, V C Chong1,2

  • 1Institute of Biological Sciences, University of Malaya, 50603, Kuala Lumpur, Malaysia.

Journal of Fish Biology
|July 2, 2016
PubMed
Summary
This summary is machine-generated.

Combining multiple 2D views of fish otoliths significantly improves classification accuracy for Sciaenidae species. This approach enhances automated fish identification systems, making them more robust and efficient.

Keywords:
Malaysiaautomated classificationgeometric morphometricsotolithsshape analysis

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Area of Science:

  • Ichthyology
  • Morphometrics
  • Computational Biology

Background:

  • Fish classification is crucial for fisheries management and biodiversity assessment.
  • Traditional methods often rely on external morphology, which can be variable.
  • Sagittal otoliths offer detailed shape information for species identification.

Purpose of the Study:

  • To evaluate the effectiveness of combining multiple 2D views of sagittal otoliths for fish classification.
  • To compare different shape description methods for otolith analysis.
  • To develop a generic content-based image retrieval (CBIR) system for otolith image searching.

Main Methods:

  • Combined multiple 2D views (proximal, anterior, ventral) of sagittal otoliths.
  • Applied shape description methods: shape indices, Procrustes analysis, and elliptical Fourier analysis.
  • Developed a CBIR system using Procrustes distance for otolith image retrieval.

Main Results:

  • Combined 2D views significantly improved classification accuracy compared to single views for nine Sciaenidae species.
  • Procrustes analysis and elliptical Fourier analysis outperformed shape indices with single views.
  • All shape description methods performed equally well with combined views.
  • The CBIR system successfully retrieved otolith images without requiring specific orientation information.

Conclusions:

  • Combining multiple 2D otolith views is a superior method for fish classification.
  • Automated CBIR systems using otolith shape are effective for fish identification.
  • This approach facilitates efficient and accurate fish species identification in ichthyological research.