research-article

Automatic fish classification for underwater species behavior understanding

Authors:

Concetto Spampinato,

Daniela Giordano,

Roberto Di Salvo,

Yun-Heh Jessica Chen-Burger,

Robert Bob Fisher,

Gayathri NadarajanAuthors Info & Claims

ARTEMIS '10: Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams

Pages 45 - 50

https://doi.org/10.1145/1877868.1877881

Published: 29 October 2010 Publication History

Abstract

The aim of this work is to propose an automatic fish classification system that operates in the natural underwater environment to assist marine biologists in understanding subehavior. Fish classification is performed by combining two types of features: 1) Texture features extracted by using statistical moments of the gray-level histogram, spatial Gabor filtering and properties of the co-occurrence matrix and 2) Shape Features extracted by using the Curvature Scale Space transform and the histogram of Fourier descriptors of boundaries. An affine transformation is also applied to the acquired images to represent fish in 3D by multiple views for the feature extraction. The system was tested on a database containing 360 images of ten different species achieving as average correct rate of about 92%. Then, fish trajectories extracted using the proposed fish classification combined with a tracking system, are analyzed in order to understand anomalous behavior. In detail, the tracking layer computer fish trajectories, the classification layer associates trajectories to fish species and then by clustering these trajectories we are able to detect unusual fish behaviors to be further investigated by marine biologists.

References

[1]

S. Abbasi and F. Mokhtarian. Robustness of shape similarity retrieval under affine transformation. In Proceedings of Challenge of Image Retrieval, Newcastle upon Tyne, 1999.

Digital Library

[2]

S. Abbasi, F. Mokhtarian, and J. Kittler. Curvature scale space image in shape similarity retrieval. MultSys, 7(6):467--476, 1999.

Digital Library

[3]

B. Benson, J. Cho, D. Goshorn, and R. Kastne. Field programmable gate array based fish detection using Haar classifiers. In American Academy of Underwater Science, 2009.

[4]

P. Brehmer, T. Do Chi, and D. Mouillot. Amphidromous fish school migration revealed by combining fixed sonar monitoring (horizontal beaming) with fishing data. Journal of Experimental Marine Biology and Ecology, 334:139--150, 2006.

[5]

D. Edgington, D. Cline, D. Davis, I. Kerkez, and J. Mariette. Detecting, tracking and classifying animals in underwater video. In OCEANS 2006, pages 1--5, 18--21 2006.

[6]

R. C. Gonzalez and R. E. Woods. Digital Image Processing (3rd Edition). Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 2006.

Digital Library

[7]

R. Larsen, H. Olafsdottir, and B. Ersboll. Shape and texture based classification of fish species. In SCIA09, pages 745--749, 2009.

Digital Library

[8]

M.-Y. Liao, D.-Y. Chen, C.-W. Sua, and H.-R. Tyan. Real-time event detection and its application to surveillance systems. In Circuits and Systems, 2006. ISCAS 2006. Proceedings. 2006 IEEE International Symposium on, pages 4 pp. 512, 0-0 2006.

[9]

J. Lin, M. Vlachos, E. Keogh, and D. Gunopulos. Iterative incremental clustering of time series. In IN EDBT, pages 106--122, 2004.

[10]

F. Mokhtarian and S. Abbasi. Shape similarity retrieval under affine transforms. Pattern Recognition, 35(1):31--41, 2002.

[11]

E. F. Morais, M. F. M. Campos, F. L. C. Padua, and R. L. Carceroni. Particle filter-based predictive tracking for robust fish counting. Computer Graphics and Image Processing, Brazilian Symposium on, 0:367--374, 2005.

Digital Library

[12]

G. Nadarajan. Planning for automatic video processing using ontology-based work flow. In ICAPS 07, 2007.

[13]

G. Nadarajan, Y.-H. Chen-Burger, and R. B. Fisher. A knowledge-based planner for processing unconstrained underwater videos. In STRUCK 09, 2009.

[14]

Y. Nagashima and T. Ishimatsu. A morphological approach to fish discrimination. In MVA98, 1998.

[15]

W. Rouse. Population dynamics of barnacles in the intertidal zone. Marine Biology Research Experiment, 2007.

[16]

A. Rova, G. Mori, and L. M. Dill. One fish, two fish, butterfish, trumpeter: Recognizing fish in underwater video. In IAPR Conference on Machine Vision Applications, 2007.

[17]

U. Soori and M. Arshad. Underwater crowd ow detection using Lagrangian dynamics. Indian Journal of Marine Science, 38:359--364, 2009.

[18]

C. Spampinato, Y.-H. Chen-Burger, G. Nadarajan, and R. B. Fisher. Detecting, tracking and counting sh in low quality unconstrained underwater videos. In VISAPP (2), pages 514--519, 2008.

Cited By

Ferrari MD’Agostino DAguzzi JMarini S(2025)Underwater Mediterranean image analysis based on the compute continuum paradigmFuture Generation Computer Systems10.1016/j.future.2024.107481162:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.future.2024.107481
Kapoor MPrummel WGiraldo JSubudhi BZakharova ABouwmans TBansal A(2025)Graph-based Moving Object Segmentation for underwater videos using semi-supervised learningComputer Vision and Image Understanding10.1016/j.cviu.2025.104290252(104290)Online publication date: Feb-2025
https://doi.org/10.1016/j.cviu.2025.104290
Fan SSong CFeng HYu Z(2024)Take good care of your fish: fish re-identification with synchronized multi-view camera systemFrontiers in Marine Science10.3389/fmars.2024.142945911Online publication date: 6-Nov-2024
https://doi.org/10.3389/fmars.2024.1429459
Show More Cited By

Index Terms

Automatic fish classification for underwater species behavior understanding
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

Recommendations

Real-time underwater sorting of edible fish species

Common carp (Cyprinus carpio), St. Peter's fish (Oreochromis sp.) and grey mullet (Mugil cephalus), were sorted according to species while swimming in pond water containing algae and suspended sediments. Fish images were acquired by a computer vision ...
Crosspooled FishNet: transfer learning based fish species classification model
Abstract
Fish species classifiction is an important task for biologists and marine ecologists to frequently estimate the relative abundance of fish species in their natural habitats and monitor changes in their populations. Traditional methods used for ...
The analysis of fish body for automatic underwater vehicle movement
CCDC'09: Proceedings of the 21st annual international conference on Chinese Control and Decision Conference

Automatic Underwater Vehicles (AUV) admires fish skilled movement in underwater because fish has a fin-based propulsion system and perfect nervous system which can perform well for both high-speed cruising and ingenious responses. The motions of AUV are ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ARTEMIS '10: Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams

October 2010

104 pages

ISBN:9781450301633

DOI:10.1145/1877868

General Chairs:
Anastasios Doulamis
Technical University of Crete, Greece
,
Jordi Gonzàlez
Universitat Autònoma de Barcelona - CVC, Spain
,
Program Chairs:
Marco Bertini
University of Firenze, Italy
,
Luc van Gool
Technische Hochschule Zürich (ETHz), Switzerland
,
Nikolaos Matsatsinis
Technical University of Crete, Greece
,
Thomas B. Moeslund
University of Aalborg, Denmark
,
Mark Nixon
University of Southampton, United Kingdom
,
Liang Wang
University of Bath, United Kingdom

Copyright © 2010 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]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tag

fish species description and classification

Qualifiers

Research-article

Conference

MM '10

Sponsor:

SIGMM

MM '10: ACM Multimedia Conference

October 29, 2010

Firenze, Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

137
Total Citations
View Citations
1,072
Total Downloads

Downloads (Last 12 months)57
Downloads (Last 6 weeks)11

Reflects downloads up to 08 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ferrari MD’Agostino DAguzzi JMarini S(2025)Underwater Mediterranean image analysis based on the compute continuum paradigmFuture Generation Computer Systems10.1016/j.future.2024.107481162:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.future.2024.107481
Kapoor MPrummel WGiraldo JSubudhi BZakharova ABouwmans TBansal A(2025)Graph-based Moving Object Segmentation for underwater videos using semi-supervised learningComputer Vision and Image Understanding10.1016/j.cviu.2025.104290252(104290)Online publication date: Feb-2025
https://doi.org/10.1016/j.cviu.2025.104290
Fan SSong CFeng HYu Z(2024)Take good care of your fish: fish re-identification with synchronized multi-view camera systemFrontiers in Marine Science10.3389/fmars.2024.142945911Online publication date: 6-Nov-2024
https://doi.org/10.3389/fmars.2024.1429459
Li DYu JDu ZXu WWang GZhao SLiu YMuhammad A(2024)Advances in the application of stereo vision in aquaculture with emphasis on fish: A reviewReviews in Aquaculture10.1111/raq.12919Online publication date: 27-Apr-2024
https://doi.org/10.1111/raq.12919
Lin HZhang WMulqueeney JBrombacher ASearle‐Barnes ANixon MCai XEzard T(2024) 3DKMI : A MATLAB package to generate shape signatures from Krawtchouk moments and an application to species delimitation in planktonic foraminifera Methods in Ecology and Evolution10.1111/2041-210X.1438815:11(1940-1948)Online publication date: 26-Sep-2024
https://doi.org/10.1111/2041-210X.14388
S HK SSuntharavadivelan M VPrabhavathy P(2024)FinSecure: Utilizing IoT Sensors for Formaldehyde Detection and Fish Freshness Detection for Enhancing Safety in Fish Consumption Using Machine Learning and Deep Learning2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)10.1109/IConSCEPT61884.2024.10627800(1-8)Online publication date: 4-Jul-2024
https://doi.org/10.1109/IConSCEPT61884.2024.10627800
Ishwarya CSasipraba TKumar APrakash S(2024)An In-Depth Analysis of Machine Learning and Deep Learning Methods for the Classification of Underwater Marine Species2024 10th International Conference on Communication and Signal Processing (ICCSP)10.1109/ICCSP60870.2024.10543497(1678-1683)Online publication date: 12-Apr-2024
https://doi.org/10.1109/ICCSP60870.2024.10543497
Abangan ABürgi KMéhault SDeroiné MKopp DFaillettaz R(2024)Assessment of sustainable baits for passive fishing gears through automatic fish behavior recognitionScientific Reports10.1038/s41598-024-63929-514:1Online publication date: 7-Jun-2024
https://doi.org/10.1038/s41598-024-63929-5
Lavanya GTeja UDussa VReddy ANitish Y(2024)Classification of Underwater Fish Species Using Custom-Built Deep Learning ArchitecturesData Science and Applications10.1007/978-981-99-7817-5_17(211-226)Online publication date: 18-Jan-2024
https://doi.org/10.1007/978-981-99-7817-5_17
Reddy PMalathi MJulaiha A(2024)Deep Fish: An Approach to Fish Species Identification Through Deep Learning TechniquesEmerging Trends in Expert Applications and Security10.1007/978-981-97-3991-2_22(261-272)Online publication date: 16-Sep-2024
https://doi.org/10.1007/978-981-97-3991-2_22
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten