Determining the Appropriate Feature Set for Fish Classification Tasks

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 corr...

Full description

Saved in:
Bibliographic Details
Published inXVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05) pp. 173 - 180
Main Authors Nery, M.S., Machado, A.M., Campos, M.F.M., Padua, F.L.C., Carceroni, R., Queiroz-Neto, J.P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Biodiversity
Forestry
Humans
Hydroelectric power generation
Information analysis
Law
Marine animals
Rivers
Robustness
Water pollution
Online AccessGet full text

Cover

More Information
Summary: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.
ISBN:9780769523897
0769523897
ISSN:1530-1834
2377-5416
DOI:10.1109/SIBGRAPI.2005.25