A complex network approach for fish species recognition based on otolith shape

Abstract

Fish otolith recognition is an essential task to study the evolution and food chains in paleontological and ecological sciences. One of the approaches to this problem is to automatically analyze the shape of otolith contour present in images. In this paper, we explore a state-of-the-art shape analysis method called “angular descriptors of complex networks (ADCN)” applied to the classification of otolith images for fish species recognition. The ADCN method models the otolith contour as a graph, or complex network, and computes angular properties from its connections for shape characterization. The ADCN method is evaluated in an otolith image dataset composed of 14 fish species from three families. Up to 95.71% of accuracy is achieved, which surpasses other literature methods and confirms that the ADCN method can be an important tool for such biological problems.