Skip to main content

Advertisement

SpringerLink
Log in

Evaluating cluster detection algorithms and feature extraction techniques in automatic classification of fish species

  • Theoretical advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

This paper proposes five different schemes for automatic classification of fish species. These schemes make the species recognition based on image sample analysis. Different techniques have been combined for building the classifiers: three feature extraction techniques (PCA, SIFT and SIFT + VLAD + PCA), three data clustering algorithms (aiNet, ARIA and k-means) and three input classifiers (k-NN, SIFT class. and k-means class) are tested. When compared to common methodologies, which are based on human observation, it is believed that these schemes are able to provide significant improvement in time and financial resources spent in classification. Two datasets have been considered: (1) a dataset with image samples of six fish species which are perfectly conserved in formaldehyde solution, and; (2) a dataset composed of images of four fish species in real-world conditions (in vivo). The five proposed schemes have been evaluated in both datasets, and a ranking for the methods has been derived for each one.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. Color images are generally composed of three-dimensional (3D) vector values.

References

  1. Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396

    Article  MATH  Google Scholar 

  2. Bermejo S, Monegal B, Cabestany J (2007) Fish age categorization from otolith images using multi-class support vector machines. Fish Res 84:247–253

    Article  Google Scholar 

  3. Bezerra GB, Barra TV, Castro LN, Zuben FJV (2005) Adaptive radius immune algorithm for data clustering. Lect Notes Comput Sci Artif Immune Syst 3627:290–303

    Article  Google Scholar 

  4. Bowen M, Marques S, Silva L, Vono V, Godinho H (2006) Comparing on site human and video counts at igarapava fish ladder, southeastern Brazil. Neotrop Ichthyol 4:291–294

    Article  Google Scholar 

  5. Cabreira AG, Tripode M, Madirolas A (2009) Artificial neural networks for fish-species identification. ICES J Mar Sci 4:291–294

    Google Scholar 

  6. Cadieux S, Lalonde F, Michaud F. (2000) Intelligent system for automated fish sorting and counting. In: Proceedings of the intelligent robots and systems—IEEE IROS, pp 1279–1284

  7. Chan D, Hockaday S, Tillett RD, Ross LG. (1999) A trainable n-tuple pattern classifier and its application for monitoring fish underwater. In: Proceedings of the internetional conference image processing and its applications, pp 255–259

  8. Chang Y, Lee DJ, Hong Y, Archibald J (2008) Unsupervised video shot detection using clustering ensemble with a color global scale-invariant feature transform descriptor. J Image Video Process 2(24):9:1–9:10

    Google Scholar 

  9. Charef A, Ohshimo S, Aoki I, Al Absi N (2009) Classification of fish schools based on evaluation of acoustic descriptor characteristics. Fish Sci 76:1–11

    Article  Google Scholar 

  10. de Castro LN, Zuben FJV (2001) aiNet: an artificial immune network for data analysis. In: Abbass HA, Sarker RA, Newton CS (eds) Data mining: a heuristic approach, chapter XII. Idea Group Publishing, USA, pp 231–259

  11. Duda RO, Hart PE (1973) Pattern classification and scene analysis. Wiley, New York

    MATH  Google Scholar 

  12. Fernandez DR, Agostinho AA, Bini LM (2004) Selection of an experimental fish ladder located at the dam of the Itaipu binacional, Paraná River, Brazil. Braz Arch Biol Technol 47(4):579–586

    Article  Google Scholar 

  13. Guan N, Tao D, Luo Z, Yuan B (2012) Online nonnegative matrix factorization with robust stochastic approximation. IEEE Trans Neural Netw Learn Syst 23(7):1087–1099

    Article  Google Scholar 

  14. Hoggarth D, Abeyasekera S, Arthur RI, Beddington JR. (2006) Stock assessment for fishery management: a framework guide to the stock assessment tools of the fisheries management science programme, paper 487 edn. FAO fisheries technical paper

  15. Jégou H, Douze M, Schmid C, Pérez P (2010) Aggregating local descriptors into a compact image representation. In: Proceedings of the IEEE conference on computer vision and Pattern recognition

  16. Ke Y, Sukthankar R. (2004) PCA–SIFT: a more distinctive representation for local image descriptors. In: Proceedings of the IEEE computer society conference computer vision and pattern recognition, pp 506–513

  17. Kuikka S, Hildén M, Gislason H, Hansson S, Sparholt H, Varis O (1999) Modeling environmentally driven uncertainties in Baltic cod (Gadus morhua) management by Bayesian influence diagrams. Can J Fish Aquat Sci 56:629–641

    Article  Google Scholar 

  18. Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791. doi:10.1038/44565

    Article  Google Scholar 

  19. Lee DJ, Redd S, Schoenberger R, Xiaoqian X, Pengcheng Z. (2003) An automated fish species classification and migration monitoring system. In: Annual conference IEEE industrial electronics society, pp 1080–1085

  20. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  21. Luo Y, Tao D, Geng B, Xu C, Maybank SJ (2013) Manifold regularized multitask learning for semi-supervised multilabel image classification. IEEE Trans Image Process 22(2):523–536

    Article  MathSciNet  Google Scholar 

  22. Luo Y, Tao D, Xu C, Li D, Xu C. (2013) Vector-valued multi-view semi-supervsed learning for multi-label image classification. In: AAAI, pp 647–653

  23. Luo Y, Tao D, Xu C, Xu C, Liu H, Wen Y (2013) Multiview vector-valued manifold regularization for multilabel image classification. IEEE Trans Neural Netw Learn Syst 24(5):709–722

    Article  Google Scholar 

  24. Macqueen JB. (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the Berkeley symposium on mathematical statistics and probability

  25. Nery M, Machado A, Campos M, Padua F, Carceroni R, Queiroz-Neto J. (2005) Determining the appropriate feature set for effective fish classification tasks. In: Brazilian symposium on computer graphics and image processing—SIBGRAPI, pp 173–180

  26. Nigsch F, Bender A, van Buuren B, Tissen J, Nigsch E, Mitchell J (2006) Melting point prediction employing k-nearest neighbor algorithms and genetic parameter optimization. J Cheml Inf Model 46:2412–2422

    Article  Google Scholar 

  27. Pearson K (1901) On lines and planes of closest fit to systems of points in space. Philos Mag 2(6):559–572

    Article  Google Scholar 

  28. Pereiro J (1995) Assessment and management of fish populations: a critical view. Sci Mar 59(3):653–660

    Google Scholar 

  29. Perronnin F, Dance C. (2007) Fisher kernels on visual vocabularies for image categorization. In: Proceedings od the IEEE conference computer vision and pattern recognition—CVPR

  30. Robothama H, Boscha P, Gutiérrez-Estradab JC, Castillo J, Pulido-Calvob I (2010) Acoustic identification of small pelagic fish species in Chile using support vector machines and neural networks. Fish Res 102:115–122

    Article  Google Scholar 

  31. Rodrigues MTA, Padua FLC, Gomes RM. (2008) Classificação de espécies de peixes baseada em sistemas imunológicos artificiais e análise de componentes principais. In: Congresso Brasileiro de Automática—CBA, pp 61–66

  32. Rodrigues MTA, Pádua FLC, Gomes RM, Soares GE. (2010) Automatic fish species classification based on robust feature extraction techniques and artificial immune systems. In: Proceedings of the international conference bio-inspired computing: theories and applications—BIC-TA

  33. Rova A, Mori G, Dill LM. (2007) One fish, two fish, butterfish, trumpeter: recognizing fish in underwater video. In: IAPR conference on machine vision applications, pp 404–407

  34. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  35. Sivic J, Zisserman A. (2003) Video Google: a text retrieval approach to object matching in videos. In: Proceedings of the international conference on computer vision—ICCV

  36. Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Article  Google Scholar 

  37. Wang F, Tan C, König AC, Li P. (2011) Efficient document clustering via online nonnegative matrix factorizations. In: SDM, SIAM / Omnipress, pp 908–919

  38. Wu M, Schölkopf B (2007) Transductive classification via local learning regularization. J Mach Learn Res Proc Track 2:628–635

    Google Scholar 

  39. Yu J, Tao D, Rui Y, Cheng J (2013) Pairwise constraints based multiview features fusion for scene classification. Pattern Recognit 46(2):483–496

    Article  MATH  Google Scholar 

  40. Yu J, Tao D, Wang M (2012) Adaptive hypergraph learning and its application in image classification. IEEE Trans Image Process 21(7):3262–3272

    Article  MathSciNet  Google Scholar 

  41. Yu J, Wang M, Tao D (2012) Semi-supervised multiview distance metric learning for cartoon synthesis. IEEE Trans Image Process 21(11):4636–4648

    Article  MathSciNet  Google Scholar 

  42. Zhou D, Huang J, Schölkopf B (2007) Learning with hypergraphs: clustering, classification, and embedding. In: Advances in neural information processing systems (NIPS) 19. Vancouver, British Columbia, Canada, pp 1601–1608

Download references

Acknowledgments

The authors thank the support of FAPEMIG-Brazil under Procs. EDT-162/07 and APQ-01180-10, CEFET-MG under Proc. No 023-076/09, CNPq-Brazil and of CAPES-Brazil.

Author information

Authors and Affiliations

  1. Department of Computing, Centro Federal de Educação Tecnológica de Minas Gerais, Av. Amazonas 7675, Belo Horizonte, MG, CEP 30510-000, Brazil

    Marco T. A. Rodrigues, Mário H. G. Freitas, Flávio L. C. Pádua & Rogério M. Gomes

  2. Department of Electrical Engineering, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, 6627, Belo Horizonte, MG, CEP 31270-010, Brazil

    Eduardo G. Carrano

Authors
  1. Marco T. A. Rodrigues
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mário H. G. Freitas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Flávio L. C. Pádua
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rogério M. Gomes
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Eduardo G. Carrano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rogério M. Gomes.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rodrigues, M.T.A., Freitas, M.H.G., Pádua, F.L.C. et al. Evaluating cluster detection algorithms and feature extraction techniques in automatic classification of fish species. Pattern Anal Applic 18, 783–797 (2015). https://doi.org/10.1007/s10044-013-0362-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-013-0362-6

Keywords

Search

Navigation