A survey on fish classification techniques

Fish classification (FC) is an expansively studied problem in the domains of image segmentation, pattern recognition, and information retrieval. It has been applied in a countless number of domains including target marketing. Meanwhile, governments are obliged to maintain the fish supply and balance between the ecosystem, commercial, agriculture field, marine scientists, and industrial arena of fish including the nutrition and canning factories. The various FC techniques performance is compared relying on the availability of preprocessing and feature extraction methods, the number of extracted features and classification accuracy, the number of fish families/species recognized. This survey also reviewed the use of Databases such as Fish4-Knowledge (F4K), knowledge database, and Global Information System (GIS) on Fishes and other FC databases. The study on preprocessing methods features extraction techniques and classifiers are gathered from recent works to enhance the understanding of the characteristics of preprocessing methods, features extraction techniques, and classifiers to guide future research directions and compensate for current research gaps. (c) 2020 The Authors. Published by Elsevier B.V. on behalf of King Saud University.

Automated summary

Fish classification is a widely studied problem in the fields of image segmentation, pattern recognition, and information retrieval. This study compares and evaluates various preprocessing methods, feature extraction techniques, and classifiers, and reviews the use of relevant databases. By collecting recent research works, it provides guidance for future research directions.