Shape geodesics for the classification of calcified structures: Beyond Fourier shape descriptors

Nasreddine, Kamal; Benzinou, Abdesslam; Fablet, Ronan

doi:10.1016/j.fishres.2009.03.008

Cited by 14 publications

(13 citation statements)

References 41 publications

(54 reference statements)

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“…Studies that applied only DA constituted ϳ92%, while one study (<1%) used DA and RF in parallel (Jones and Checkley 2017). The remaining ϳ7% of the publications applied classifiers other than DA to assign samples to their respective class (e.g., SVM or KNN classifier (Reig-Bolaño et al 2010b;Benzinou et al 2013), boundary-based shape classification (Nasreddine et al 2009), between-class correspondence analysis (Ponton 2006), or RF (e.g., Zhang et al 2016)).…”

Section: Literature Review Of the Use Of Statistical Classifiersmentioning

confidence: 99%

“…Otolith shape is mostly driven by a combination of environmental and genetic factors and contains stock-specific features, which are usable as a relevant marker of distinct stocks (Vieira et al 2014;Berg et al 2018). In recent years, diverse methods enabling the description of the otolith shape were developed and tested, such as curvaturebased descriptors, wavelets, shape geodesics, or mirroring techniques (Parisi-Baradad et al 2005;Nasreddine et al 2009;Harbitz and Albert 2015). However, otolith outlines are still most frequently investigated with a mathematical scheme of Fourier decomposition, namely fast Fourier transform or elliptical Fourier analysis (Stransky 2014).…”

Section: Introductionmentioning

confidence: 99%

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Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shape

Smoliński

Schade

Berg

2020

Can. J. Fish. Aquat. Sci.

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The assignment of individual fish to its stock of origin is important for reliable stock assessment and fisheries management. Otolith shape is commonly used as the marker of distinct stocks in discrimination studies. Our literature review showed that the application and comparison of alternative statistical classifiers to discriminate fish stocks based on otolith shape is limited. Therefore, we compared the performance of two traditional and four machine learning classifiers based on Fourier analysis of otolith shape using selected stocks of Atlantic cod (Gadus morhua) in the southern Baltic Sea and Atlantic herring (Clupea harengus) in the western Norwegian Sea, Skagerrak, and the southern Baltic Sea. Our results showed that the stocks can be successfully discriminated based on their otolith shapes. We observed significant differences in the accuracy obtained by the tested classifiers. For both species, support vector machines (SVM) resulted in the highest classification accuracy. These findings suggest that modern machine learning algorithms, like SVM, can help to improve the accuracy of fish stock discrimination systems based on the otolith shape.

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Section: Literature Review Of the Use Of Statistical Classifiersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shape

Smoliński

Schade

Berg

2020

Can. J. Fish. Aquat. Sci.

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“…Geodesic approach was recently proposed in [11], [14] and it proved to give very good performances on different shapes and in particular on otolith shapes. In most cases of studying the shapes of calcified structures, we can extract points of specific features or attributes that correspond to similar points of other shapes.…”

Section: Geodesic Approachmentioning

confidence: 99%

“…Formally, the numerical computation of d(Γ 1 , Γ 2 ) is solved by using a dynamic programming technique (refer to [14] for more details). Once the distances are calculated, shape classification can be performed by taking the distance matrix as input for a K-Nearest Neighbours (KNN) classifier [13].…”

Section: Geodesic Approachmentioning

confidence: 99%

Otolith shape classification for fish stock discrimination

Soria

Nasreddine

Parisi-Baradad

et al. 2014

International Image Processing, Applications and Systems Conference

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The shape analysis of otoliths, which are calcified\ud structures in the inner ear of teleostean fishes, is known to be\ud particularly relevant to address species identification and stock\ud discrimination. Generally, scientists use classical methodologies\ud of statistical analysis and shape recognition such as Fourier\ud shape descriptors and Principal Component Analysis (PCA).\ud These methods are subject to several limitations mainly to their\ud incapacity to locate irregularities because they are based on global\ud characterization of shape. Recently, more advanced techniques\ud are proposed in this context in order to improve classification\ud accuracies. The first recent method exploits the potential of shape\ud geodesics which rely on local shape features for classification\ud issues. The second one addresses the Best-Basis paradigm which\ud combines the Wavelet Transform, and the potential of statistical\ud analysis in order to fully automate the selection process of efficient\ud features for classification. These methods have been shown to\ud significantly outperform the standard approaches but they are not\ud compared together yet. This study compare these two methods on\ud a real dataset. The comparison is performed on\ud 600\ud striped red\ud mullet calcified structures collected for the NESPMAN European\ud project. For each method, performances are reported for the\ud classification of samples coming from three geographical zones\ud in the Northwest European seas: the Bay of Biscay, a mixing zone\ud composed of the Celtic Sea and the Western English Channel and\ud a northern zone composed of the Eastern English Channel and\ud the North Sea. Comparison shows that both methods lead to same\ud conclusions.Peer ReviewedPostprint (published version

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“…In another work, Ponton (2006) compared EFDs with geometric morphometrics based on landmarks and semilandmarks for species identification and to visualize how the otolith shape changes during growth. Shape geodesics are another recent shape descriptor approach which outperforms the standard Fourier approaches in classification results (Nasreddine et al 2009). …”

mentioning

confidence: 99%

A new otolith image contour descriptor based on partial reflection

et al. 2010

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In this paper we propose a new contour\ud descriptor for reconstructing fish otolith contours that\ud uses half the number of coefficients needed by the\ud classical elliptical Fourier descriptors (EFDs) for the\ud same accuracy. The efficiency of the proposed shape\ud descriptor has been tested with two different species,\ud Liza aurata and Liza ramada, belonging to the family\ud Mugilidae (http://aforo.cmima.csic.es), and two populations\ud (from the USA and Canada) of the family\ud Merlucciidae. These groups are characterized by high\ud similarity between species; therefore, accurate, detailed\ud shape analyses of their otoliths can help to identify and\ud discriminate morphologically close species or different\ud populations. For comparative purposes the descriptor\ud was also tested with specimens of Mullus barbatus\ud (Family Mullidae). For a certain number of coefficients\ud (<50) the new descriptor clearly outperforms the\ud reconstruction accuracy of the EFD.Peer ReviewedPostprint (published version

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Shape geodesics for the classification of calcified structures: Beyond Fourier shape descriptors

Cited by 14 publications

References 41 publications

Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shape

Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shape

Otolith shape classification for fish stock discrimination

A new otolith image contour descriptor based on partial reflection

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