Automated classification of fish using machine learning and pattern recognition

Abstract

Automation in classification is a ubiquitous process in every modern industry. This process is especially critical in the seafood industry of raw fish, where nature produces magnitude of complex forms and patterns. This paper is presenting the various methods of pattern recognition to the fish classification problem, discusses the processing of images, decision-making methods for classification of patterns (Nearest Neighbor Algorithm and Bayesian decision theory), optimization of the classifiers for pattern recognition, ways of analysis of patterns by groups, and the basic concepts of K-means clustering algorithm.