research-article

Fish Species Identification in Real-Life Underwater Images

Authors:

Simone Palazzo,

Francesca MurabitoAuthors Info & Claims

MAED '14: Proceedings of the 3rd ACM International Workshop on Multimedia Analysis for Ecological Data

Pages 13 - 18

https://doi.org/10.1145/2661821.2661822

Published: 07 November 2014 Publication History

Abstract

Kernel descriptors consist in finite-dimensional vectors extracted from image patches and designed in such a way that the dot product approximates a nonlinear kernel, whose projection feature space would be high-dimensional. Recently, they have been successfully used for fine-gradined object recogntion, and in this work we study the application of two such descriptors, called EMK and KDES (respectively designed as a kernelized generalization of the common bag-of-words and histogram-of-gradient approaches) to the MAED 2014 Fish Classification task, consisting of about 50,000 underwater images from 10 fish species.

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Cited By

Qin XYu CLiu BZhang Z(2024)YOLO8-FASG: A High-Accuracy Fish Identification Method for Underwater Robotic SystemIEEE Access10.1109/ACCESS.2024.340486712(73354-73362)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3404867
Reddy Nandyala NKumar Sanodiya R(2023)Underwater Object Detection Using Synthetic Data2023 11th International Symposium on Electronic Systems Devices and Computing (ESDC)10.1109/ESDC56251.2023.10149870(1-6)Online publication date: 4-May-2023
https://doi.org/10.1109/ESDC56251.2023.10149870
Ali KMoetesum MSiddiqi IMahmood N(2022)Marine Object Detection using Transformers2022 19th International Bhurban Conference on Applied Sciences and Technology (IBCAST)10.1109/IBCAST54850.2022.9990099(951-957)Online publication date: 16-Aug-2022
https://doi.org/10.1109/IBCAST54850.2022.9990099
Show More Cited By

Index Terms

Fish Species Identification in Real-Life Underwater Images
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition

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Published In

cover image ACM Conferences

MAED '14: Proceedings of the 3rd ACM International Workshop on Multimedia Analysis for Ecological Data

November 2014

46 pages

ISBN:9781450331234

DOI:10.1145/2661821

General Chairs:
Concetto Spampinato
University of Catania, Italy
,
Vasileios Mezaris
CERTH-ITI, Greece
,
Marco Cristani
University of Verona, Italy

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2014

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MM '14

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SIGMM

MM '14: 2014 ACM Multimedia Conference

November 7, 2014

Florida, Orlando, USA

Acceptance Rates

MAED '14 Paper Acceptance Rate 6 of 11 submissions, 55%;

Overall Acceptance Rate 13 of 23 submissions, 57%

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Cited By

Qin XYu CLiu BZhang Z(2024)YOLO8-FASG: A High-Accuracy Fish Identification Method for Underwater Robotic SystemIEEE Access10.1109/ACCESS.2024.340486712(73354-73362)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3404867
Reddy Nandyala NKumar Sanodiya R(2023)Underwater Object Detection Using Synthetic Data2023 11th International Symposium on Electronic Systems Devices and Computing (ESDC)10.1109/ESDC56251.2023.10149870(1-6)Online publication date: 4-May-2023
https://doi.org/10.1109/ESDC56251.2023.10149870
Ali KMoetesum MSiddiqi IMahmood N(2022)Marine Object Detection using Transformers2022 19th International Bhurban Conference on Applied Sciences and Technology (IBCAST)10.1109/IBCAST54850.2022.9990099(951-957)Online publication date: 16-Aug-2022
https://doi.org/10.1109/IBCAST54850.2022.9990099
Kottursamy K(2021)Multi-scale CNN Approach for Accurate Detection of Underwater Static Fish ImageJournal of Artificial Intelligence and Capsule Networks10.36548/jaicn.2021.3.0063:3(230-242)Online publication date: 30-Aug-2021
https://doi.org/10.36548/jaicn.2021.3.006
Athira. PMithun Haridas TSupriya M(2021)Underwater Object Detection model based on YOLOv3 architecture using Deep Neural Networks2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS)10.1109/ICACCS51430.2021.9441905(40-45)Online publication date: 19-Mar-2021
https://doi.org/10.1109/ICACCS51430.2021.9441905
P PT RK VR ST T(2020)Fish Species Classification using SVM KernelsInnovations in Information and Communication Technology Series10.46532/978-81-950008-1-4_102(466-470)Online publication date: 30-Dec-2020
https://doi.org/10.46532/978-81-950008-1-4_102
Salman ASiddiqui SShafait FMian AShortis MKhurshid KUlges ASchwanecke U(2019)Automatic fish detection in underwater videos by a deep neural network-based hybrid motion learning systemICES Journal of Marine Science10.1093/icesjms/fsz02577:4(1295-1307)Online publication date: 27-Feb-2019
https://doi.org/10.1093/icesjms/fsz025
Rachmatullah MSupriana I(2018)Low Resolution Image Fish Classification Using Convolutional Neural Network2018 5th International Conference on Advanced Informatics: Concept Theory and Applications (ICAICTA)10.1109/ICAICTA.2018.8541313(78-83)Online publication date: Aug-2018
https://doi.org/10.1109/ICAICTA.2018.8541313
Beyan CKatsageorgiou VFisher R(2017)Extracting statistically significant behaviour from fish tracking data with and without large dataset cleaningIET Computer Vision10.1049/iet-cvi.2016.046212:2(162-170)Online publication date: 18-Dec-2017
https://doi.org/10.1049/iet-cvi.2016.0462
Spampinato CMezaris VCristani MHua KRui YSteinmetz RHanjalic ANatsev AZhu W(2014)Summary Abstract for the 3rd ACM International Workshop on Multimedia Analysis for Ecological DataProceedings of the 22nd ACM international conference on Multimedia10.1145/2647868.2647878(1261-1262)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2647868.2647878

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