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Determining the Appropriate Feature Set for Fish Classification Tasks

M. S. NeryA. M. MachadoMario Fernando Montenegro CamposFlávio L. C. PáduaRodrigo L. CarceroniJosé P. Queiroz-Neto

We present a novel fish classification methodology based on a robust feature selection technique. Unlike existing works for fish classification, which propose descriptors and do not analyze their individual impacts in the whole classification task, we propose a general set of features and their correspondent weights that should be used as a priori information by the classifier. In this sense, instead of studying techniques for improving the classifiers structure itself, we consider it as a "black box" and focus our research in the determination of which input information must bring a robust fish discrimination. All the experiments were performed with fish species of Rio Grande river in Minas Gerais, Brazil. This work has been developed as part of a wider research [3], which has as main goal the development of effective fish ladders for the Brazilian dams.

http://doi.ieeecomputersociety.org/10.1109/SIBGRAPI.2005.25

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