Automatic Classification of Morphologically Similar Fish Species Using Their Head Contours

• Main Authors: Pere Marti-Puig, Amalia Manjabacas, Antoni Lombarte
• Format: Article
• Language: English
• Published: MDPI AG 2020-05-01
• Series: Applied Sciences
• Subjects: open contours; similarly shaped fish species; Discrete Cosine Transform (DCT); Discrete Fourier Transform (DFT); Extreme Learning Machines (ELM); feature engineering
• Online Access: https://www.mdpi.com/2076-3417/10/10/3408

This work deals with the task of distinguishing between different Mediterranean demersal species of fish that share a remarkably similar form and that are also used for the evaluation of marine resources. The experts who are currently able to classify these types of species do so by considering only a segment of the contour of the fish, specifically its head, instead of using the entire silhouette of the animal. Based on this knowledge, a set of features to classify contour segments is presented to address both a binary and a multi-class classification problem. In addition to the difficulty present in successfully discriminating between very similar forms, we have the limitation of having small, unreliably labeled image data sets. The results obtained were comparable to those obtained by trained experts.