# Automatic Classification of Morphologically Similar Fish Species Using Their Head Contours
## 1 Introduction
Thus, it is not possible to determine the statistics of individualized species of fish caught from the fishing industry, which adds error in the mathematical models of each species. In consequence, the correct identification of many specimens of these sympatric species requires an experienced observer and, as mentioned before, is a highly time-consuming task. A more complex case occurs in Mediterranean Gurnard fish (in the family of triglids and peristids), consisting of nine sympatric species (_C. cuculus_, _C. lucerna_, _C. obscurs_, _E. gurardus_, _L. cavilone_, _L. dieuzeidei_, _P. cataphractum_, _T. lyna_, and _T. lastoviza_), which are rinsed through trawl fishing on continental shelves and upper slopes, and are usually considered as only one species or an unidentified mix of species in commercial capture statistics (Fisheries Database of the Catalan Government). Morphological differences between species are based on trends of the lateral line, scale distribution, head shape and preorbital bones [15], and a high degree of expertise is required to classify triglid species. To solve both classification problems, we used a traditional machine learning (ML) workflow to create the models, which are the trained programs that predict the outputs based on a set of given inputs, also called features. That implies the design of such features as a first step, followed by the task of training the model. Conventional ML techniques include support vector machines (SVM), ensembled methods, decision trees, and also the extreme learning machines (ELM) used in this work, among others. In this context, ELM performs very similarly to SVM, with the added advantage of having a much quicker training process in comparison. In contrast to ML, an approach based on deep learning (DL) techniques exists in which the algorithms automatically learn which of the features are useful. The term _deep_ refers to the multiple _layers_ the networks have between inputs and outputs. In order to train models with DL techniques, a large amount of tagged data is required, which is complicated in our case as we work with a small dataset. Several parameterization strategies have been previously explored for closed contours, and therefore an abundance of literature is available. An important part of the transformed methods dealing with closed contours is based on the Fourier transform. Among those methods, _elliptic Fourier descriptors_ (EDF) is one of the most popular ones. This method was introduced by [PERSON] [16], and some variations have been introduced since. As applications that require shape quantification are present in a wide range of different fields, an extensive amount of these can be found [17; 18; 19; 20]. Fourier methods applied to periodic sequences (which can be associated with closed contours) exhibit good information-packing properties in the sense that they concentrate most of the signal energy in a few coefficients. Those few coefficients are employed as features for classifying purposes. The problem under discussion cannot be solved if the specimens are treated by considering their entire silhouette independently of the contour description technique employed, as is the case of multiple spatial scales methods curvature scale space (CSS) [21; 22; 23], or from the _discrete wavelet transform_ (DWT) [24]. Therefore, knowing how the specialists solve the problem, the approach that will be used will imitate the one used by these experts and thus only consider the contour fragments of interest. Unfortunately, in the context of shape analysis, there is less work done on open contours and discrete transforms. Something that is well known, however, is that Fourier-based methods do not work well when the sequences are not periodic [25]. Different approaches can be used to deal with segment contours (open contours). In Reference [26], a wavelet analysis with cubic B-spline bases was used in order to reduce disturbances and confine them around the discontinuities, and in Reference [27], the windowed short time Fourier transform (STFT) was employed.