Automatic fish species classification based on robust feature extraction techniques and artificial immune systems

This paper addresses the problem of automatic classification of fish species, by using image analysis techniques and artificial immune systems. Unlike most common methodologies, which are based on manual estimations that lead to significant time and financial constraints, we present an automatic framework based on (i) two well-known robust feature extraction techniques: Scale-Invariant Feature Transform and Principal Component Analysis for parameterizing shape, appearance and motion, (ii) two immunological algorithms: Artificial Immune Network and Adaptive Radius Immune Algorithm for clustering individuals of the same species, and (iii) a simple nearest neighbor classification strategy. The framework was successfully validated with images of fish species that have significant economic impact, achieving overall accuracy as high as 92%.