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Hierarchical Classification System with Reject Option for Live Fish Recognition

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Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 104))

Abstract

This chapter presents a Balance-Guaranteed Optimized Tree with Reject option (BGOTR) for live fish recognition in a non-constrained environment. It recognizes the top 15 common species of fish and detects new species in an unrestricted natural environment recorded by underwater cameras. This system can assist ecological surveillance research, e.g., obtaining fish population statistics from the open sea. BGOTR is automatically constructed based on inter-class similarities. We apply a Gaussian Mixture Model (GMM) and Bayes rule as a reject option after hierarchical classification—we estimate the posterior probability of being a certain species and then filter out less confident decisions. The proposed BGOTR-based hierarchical classification method achieves significant improvements compared to state-of-the-art techniques on a live fish image dataset of 24,150 manually labeled images from the south Taiwan sea.

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Authors and Affiliations

  1. Google, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA

    Phoenix X. Huang

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  1. Phoenix X. Huang
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Correspondence to Phoenix X. Huang .

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Editors and Affiliations

  1. School of Informatics, University of Edinburgh, Edinburgh, United Kingdom

    Robert B. Fisher

  2. Heriot-Watt University, Edinburgh, United Kingdom

    Yun-Heh Chen-Burger

  3. Universita Degli Studi Di Catania, Catania, Italy

    Daniela Giordano

  4. Informatica (CWI), Centrum Wiskunde &, Amsterdam, The Netherlands

    Lynda Hardman

  5. High Performance Computing, National Center of, HsinChu, Taiwan

    Fang-Pang Lin

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Huang, P.X. (2016). Hierarchical Classification System with Reject Option for Live Fish Recognition. In: Fisher, R., Chen-Burger, YH., Giordano, D., Hardman, L., Lin, FP. (eds) Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data. Intelligent Systems Reference Library, vol 104. Springer, Cham. https://doi.org/10.1007/978-3-319-30208-9_11

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  • DOI: https://doi.org/10.1007/978-3-319-30208-9_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30206-5

  • Online ISBN: 978-3-319-30208-9

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