nach oben

Pattern Analysis and Applications

Erschienen in:

01.11.2015 | Theoretical advances

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

verfasst von: Marco T. A. Rodrigues, Mário H. G. Freitas, Flávio L. C. Pádua, Rogério M. Gomes, Eduardo G. Carrano

Erschienen in: Pattern Analysis and Applications | Ausgabe 4/2015

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Vorheriger Artikel Linearizing layers of radial binary classifiers with movable centers

Nächster Artikel Evaluating classifier combination in object classification

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

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

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

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

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

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 4:291–294CrossRef

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

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

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

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

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

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 YorkMATH

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):579–586CrossRef

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):1087–1099CrossRef

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 56:629–641CrossRef

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 CrossRef

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):91–110CrossRef

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):523–536MathSciNetCrossRef

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):709–722CrossRef

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 46:2412–2422CrossRef

27.

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

28.

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

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 102:115–122CrossRef

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):2323–2326CrossRef

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):2319–2323CrossRef

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

39.

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

40.

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

41.

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

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

Titel: Evaluating cluster detection algorithms and feature extraction techniques in automatic classification of fish species
verfasst von: Marco T. A. Rodrigues
Mário H. G. Freitas
Flávio L. C. Pádua
Rogério M. Gomes
Eduardo G. Carrano
Publikationsdatum: 01.11.2015
Verlag: Springer London
Erschienen in: Pattern Analysis and Applications / Ausgabe 4/2015
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-013-0362-6

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 4/2015

Linearizing layers of radial binary classifiers with movable centers

The k-NN classifier and self-adaptive Hotelling data reduction technique in handwritten signatures recognition

Avoiding staff removal stage in optical music recognition: application to scores written in white mensural notation

Fuzzy segmentation for finger vessel pattern extraction of infrared images

Combining local and global learners in the pairwise multiclass classification

A new random approach for initialization of the multiple restart EM algorithm for Gaussian model-based clustering

Premium Partner