research-article

Fish Species Recognition from Video using SVM Classifier

Authors:

Diane Lingrand,

Frédéric PreciosoAuthors Info & Claims

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

Pages 1 - 6

https://doi.org/10.1145/2661821.2661827

Published: 07 November 2014 Publication History

Abstract

To build a detailed knowledge of the biodiversity, the geographical distribution and the evolution of the alive species is essential for a sustainable development and the preservation of this biodiversity. Massive databases of underwater video surveillance have been recently made available for supporting designing algorithms targeting the identification of fishes. However these video datasets are rather poor in terms of video resolution, pretty challenging regarding both the natural phenomena to be considered such as murky water, seaweed moving the water current, etc, and the huge amount of data to be processed. We have designed a processing chain based on background segmentation, selection keypoints with an adaptive scale, description with OpponentSift and learning of each species by a binary linear Support Vector Machines classifier.

Our algorithm has been evaluated in the context of our participation to the Fish task of the LifeCLEF2014 challenge. Compared to the baseline designed by the LifeCLEF challenge organizers, our approach reaches a better precision but a worse recall. Our performances in terms of species recognition (based only on the correctly detected bounding boxes) is comparable to the baseline, but our bounding boxes are often too large and our score is so penalized. Our results are thus really encouraging.

References

[1]

M. A. R. Ahad, T. Ogata, J. K. Tan, H. Kim, and S. Ishikawa. Motion recognition approach to solve overwriting in complex actions. In FG, pages 1--6. IEEE, 2008.

[2]

S. Ali and M. Shah. Human action recognition in videos using kinematic features and multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell., 32(2):288--303, Feb. 2010.

Digital Library

[3]

O. Barnich and M. Van Droogenbroeck. ViBe: A universal background subtraction algorithm for video sequences. IEEE Transactions on Image Processing, 20(6):1709--1724, June 2011.

Digital Library

[4]

S. Concetto, B. Fisher, and B. Boom. Lifeclef fish identification task 2014. In CLEF working notes 2014, 2014.

[5]

P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie. Behavior recognition via sparse spatio-temporal features. In Proceedings of the 14th International Conference on Computer Communications and Networks, ICCCN '05, pages 65--72, Washington, DC, USA, 2005. IEEE Computer Society.

Digital Library

[6]

A. A. Efros, A. C. Berg, G. Mori, and J. Malik. Recognizing action at a distance. In Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2, ICCV '03, pages 726--, Washington, DC, USA, 2003. IEEE Computer Society.

Digital Library

[7]

A. Joly, H. Müller, H. Goëau, H. Glotin, C. Spampinato, A. Rauber, P. Bonnet, W.-P. Vellinga, and B. Fisher. Lifeclef 2014: multimedia life species identification challenges. In Proceedings of CLEF 2014, 2014.

[8]

A. Klaeser, M. Marszalek, and C. Schmid. A spatio-temporal descriptor based on 3d-gradients. In Proceedings of the British Machine Vision Conference, pages 99.1--99.10. BMVA Press, 2008.

[9]

I. Laptev. Local Spatio-Temporal Image Features for Motion Interpretation. PhD thesis, Department of Numerical Analysis and Computer Science (NADA), KTH, 2004.

[10]

I. Laptev and T. Lindeberg. Local descriptors for spatio-temporal recognition. In In First International Workshop on Spatial Coherence for Visual Motion Analysis, 2004.

Digital Library

[11]

M. Piccardi. Background subtraction techniques: a review. In IEEE International Conference on Systems, Man, and Cybernetics, pages 3099--3104. IEEE, 2004.

[12]

R. Polana and R. Nelson. Low level recognition of human motion (or how to get your man without finding his body parts). In In Proc. of IEEE Workshop on Motion of Non-Rigid and Articulated Objects, pages 77--82. Press, 1994.

[13]

M. D. Rodriguez, J. Ahmed, and M. Shah. Action mach a spatio-temporal maximum average correlation height filter for action recognition. In CVPR. IEEE Computer Society, 2008.

[14]

C. Tanase. Towards effective spatio-temporal analysis for content-based video retrieval. PhD thesis, Thesis, 04 2014.

[15]

C. Thurau and V. Hlavác. Pose primitive based human action recognition in videos or still images. In CVPR'08, pages --1--1, 2008.

[16]

K. E. A. van de Sande, T. Gevers, and C. G. M. Snoek. Evaluation of color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9):1582--1596, Sept. 2010.

Digital Library

[17]

A. Vedaldi and B. Fulkerson. VLFeat: An open and portable library of computer vision algorithms, 2008.

[18]

G. Willems, T. Tuytelaars, and L. Gool. An efficient dense and scale-invariant spatio-temporal interest point detector. In Proceedings of the 10th European Conference on Computer Vision: Part II, ECCV '08, pages 650--663, Berlin, Heidelberg, 2008. Springer-Verlag.

Digital Library

[19]

L. Zelnik-Manor and M. Irani. Event-based analysis of video. In In Proc. of CVPR, pages 123--130, 2001.

[20]

Z. Zivkovic. Improved adaptive gaussian mixture model for background subtraction. In Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02, ICPR '04, pages 28--31, Washington, DC, USA, 2004. IEEE Computer Society.

Digital Library

Cited By

Lu JZhang SZhao SLi DZhao R(2024)A Metric-Based Few-Shot Learning Method for Fish Species Identification with Limited SamplesAnimals10.3390/ani1405075514:5(755)Online publication date: 28-Feb-2024
https://doi.org/10.3390/ani14050755
Chen LXie YLi YXu QDong J(2024)CWSCNet: Channel-Weighted Skip Connection Network for Underwater Object DetectionIEEE Transactions on Image Processing10.1109/TIP.2024.345724633(5206-5218)Online publication date: 2024
https://doi.org/10.1109/TIP.2024.3457246
Lin YXie SGhose DLiu XYou JKorhonen JLiu JDash S(2024)FishIR: Identifying Pufferfish Individual Based on Deep Learning and Face RecognitionIEEE Access10.1109/ACCESS.2024.339041212(59807-59817)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3390412
Show More Cited By

Index Terms

Fish Species Recognition from Video using SVM Classifier
1. Information systems
  1. Information systems applications

Recommendations

WikiFish: Mobile App for Fish Species Recognition Using Deep Convolutional Neural Networks
CIIS '21: Proceedings of the 2021 4th International Conference on Computational Intelligence and Intelligent Systems

Consumers of the fish market around the world face problems in the identification of fish species and people need to obtain expert assistance to do so. This situation is typically the same in Gaza Strip, the local fish market lacks such an application ...
Broadleaf species recognition with in situ hyperspectral data

Timely and accurate identification of tree species by spectral methods is crucial for forest and urban ecological management. In this study, a total of 394 reflectance spectra (between 350 and 2500 nm) from foliage branches or canopy of 11 important ...
Fish biorobotics
IROS'09: Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems

There are over 28,000 species of fishes, and a key feature of this remarkable evolutionary diversity is a great variety of propulsive systems used by fishes for maneuvering in the aquatic environment. Fishes have numerous control surfaces (fins) which ...

Comments

Information & Contributors

Information

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]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MM '14

Sponsor:

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%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
162
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)2

Reflects downloads up to 01 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Lu JZhang SZhao SLi DZhao R(2024)A Metric-Based Few-Shot Learning Method for Fish Species Identification with Limited SamplesAnimals10.3390/ani1405075514:5(755)Online publication date: 28-Feb-2024
https://doi.org/10.3390/ani14050755
Chen LXie YLi YXu QDong J(2024)CWSCNet: Channel-Weighted Skip Connection Network for Underwater Object DetectionIEEE Transactions on Image Processing10.1109/TIP.2024.345724633(5206-5218)Online publication date: 2024
https://doi.org/10.1109/TIP.2024.3457246
Lin YXie SGhose DLiu XYou JKorhonen JLiu JDash S(2024)FishIR: Identifying Pufferfish Individual Based on Deep Learning and Face RecognitionIEEE Access10.1109/ACCESS.2024.339041212(59807-59817)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3390412
Bhalla SKumar AKushwaha R(2024)Analysis of recent techniques in marine object detection: a reviewMultimedia Tools and Applications10.1007/s11042-024-19782-9Online publication date: 11-Jul-2024
https://doi.org/10.1007/s11042-024-19782-9
Waghumbare ADhake KSingh U(2023)Classification of Underwater Sea Species Using Convolutional Neural Network2023 IEEE Pune Section International Conference (PuneCon)10.1109/PuneCon58714.2023.10450029(1-5)Online publication date: 14-Dec-2023
https://doi.org/10.1109/PuneCon58714.2023.10450029
Liu YXu YWang HLi X(2023)Benthic Organism Detection, Quantification and Seamount Biology Detection Based on Deep LearningArtificial Intelligence Oceanography10.1007/978-981-19-6375-9_16(323-346)Online publication date: 4-Feb-2023
https://doi.org/10.1007/978-981-19-6375-9_16
Lin YChu ZKorhonen JXu JLiu XLiu JLiu MFang LYang WGhose DYou J(2023)Fast Accurate Fish Recognition with Deep Learning Based on a Domain-Specific Large-Scale Fish DatasetMultiMedia Modeling10.1007/978-3-031-27077-2_40(515-526)Online publication date: 29-Mar-2023
https://doi.org/10.1007/978-3-031-27077-2_40
Mathias AR NDhanalakshmi S(2022)Tracking of Underwater Objects with Occlusion Awareness using an Adaptive DEEP SORT and GMM ApproachOCEANS 2022 - Chennai10.1109/OCEANSChennai45887.2022.9775470(1-5)Online publication date: 21-Feb-2022
https://doi.org/10.1109/OCEANSChennai45887.2022.9775470
Mathias ADhanalakshmi SKumar R(2022)Occlusion aware underwater object tracking using hybrid adaptive deep SORT -YOLOv3 approachMultimedia Tools and Applications10.1007/s11042-022-13281-581:30(44109-44121)Online publication date: 31-May-2022
https://doi.org/10.1007/s11042-022-13281-5
Deep BDash R(2019)Underwater Fish Species Recognition Using Deep Learning Techniques2019 6th International Conference on Signal Processing and Integrated Networks (SPIN)10.1109/SPIN.2019.8711657(665-669)Online publication date: Mar-2019
https://doi.org/10.1109/SPIN.2019.8711657
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