Shape- and Texture-Based Fish Image Recognition System

Authors

  • Chomtip Pornpanomchai Faculty of Information and Communication Technology, Mahidol University, Bangkok 10400, Thailand.
  • Benjamaporn Lurstwut Faculty of Information and Communication Technology, Mahidol University, Bangkok 10400, Thailand.
  • Pimprapai Leerasakultham Faculty of Information and Communication Technology, Mahidol University, Bangkok 10400, Thailand.
  • Waranat Kitiyanan Faculty of Information and Communication Technology, Mahidol University, Bangkok 10400, Thailand.

Keywords:

fish features, fish recognition, image processing, pattern recognition, artificial neural networks

Abstract

This research developed a computer system capable of recognizing some fish images. The system known as the “shape- and texture-based fish image recognition system” (FIRS) consists of five subsystems—namely: 1) image acquisition, 2) image preprocessing 3) feature extraction, 4) image recognition and 5) result presentation. The experiment was conducted on 30 fish species, which consisted of 600 fish images as the training dataset and 300 fish images for testing. The system compared two recognition techniques—a Euclidean distance method (EDM) and artificial neural networks (ANN). The system was able to recognize all 30 species of the training fish images with a precision of 99.00 and 81.67% for the ANN and the EDM techniques, respectively. The average access times were 24.4 and 154.43 sec per image for the EDM and ANN techniques, respectively.

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Published

2013-08-31

How to Cite

Pornpanomchai, Chomtip, Benjamaporn Lurstwut, Pimprapai Leerasakultham, and Waranat Kitiyanan. 2013. “Shape- and Texture-Based Fish Image Recognition System”. Agriculture and Natural Resources 47 (4). Bangkok, Thailand:624-34. https://li01.tci-thaijo.org/index.php/anres/article/view/243105.

Issue

Vol. 47 No. 4 (2013): July-August

Section

Research Article

License

online 2452-316X print 2468-1458/Copyright © 2022. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/),
production and hosting by Kasetsart University of Research and Development Institute on behalf of Kasetsart University.