# Automatic Classification of Morphologically Similar Fish Species Using Their Head Contours
## 4 Conclusions  
In this work, a method was presented that reproduced the procedure that experts perform manually to separate species of fish that exhibit a very similar shape. The technique consists of classifying a fragment from the profile of fish contours. Thus, a way to parameterise open contours was developed based on both the DFT and the DCT transformations, which already allow the discrimination of contour fragments with the use of only five parameters. The presented approach for the _Mullus_ binary classification case can be compared to the seminal work of [PERSON] [10], which based on handmade measurements of the angle of head contour obtained an 88% correct distinction between both species. The mean accuracy values obtained (89.4%) in this work slightly improved those results. In addition to the difficulty of discriminating between very similar forms, we also had the limitation of having small, unreliably labeled image data sets. For the second case considered in this work, the multi-class classification problem, it is important to note that there have been no handmade studies carried out that would allow for the comparison of results. The specimens of the database were cataloged at the ICM (Institut de Ciencies del Mar) in Barcelona while different types of studies were being conducted. Given the complexity of the problem, the average classification results were close to 80%, which is in line with what trained professionals obtain when they take on the identification and classification task from photographs. If the classification is considered by classes, we observed that the class of _Lepidotrigla deuzeidei_ was very difficult to classify and thus caused the global accuracy to decrease; however, for the rest of the classes, high correct classification rates were reached. Our goal was to prove that it is possible to automatically achieve results similar to those obtained by specialists when it comes to classifying very similarly-shaped species of fish. The goal achieved was to automatically classify the images of the specimens in the database into their species. Given the physical similarity between species and the size of the database, this is a difficult objective, which (as we mentioned before) we have overcome with success comparable to that obtained by the expert. The results suggest that we should expect even better results when working with species with more differentiated forms. For future work, a far more ambitious goal involving government administrations, which must be addressed incrementally, is to computerize the analysis of fish boxes sold at fish markets. In these boxes, fish are stacked in a non-orderly fashion, with the body parts partially hidden but usually with their heads being visible. Thus, the classification of specimens based only on their head contour seems to be indeed an important improvement to be made. These studies are important as fish markets are beginning to develop projects with the aim of photographing all fish boxes sold, followed by these images being then registered containing information on the labeling, sale prices, and so forth. These large amounts of images can only be processed automatically. As long as the techniques for extracting information from images continues improving, we will be able to obtain a gradually deeper understanding on the exploitation of marine resources. This work is a pioneer in the classification of biological forms through contour segments. In this paper, the identification of the contour fragments was made with the help of marks. In this way, we were sure to deal with the fragment used by the experts. In the type of images used, the marks were located in easily identifiable positions and, even though this was not the purpose of this work, it was even shown in a prior subsection that these are not entirely necessary.