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Pattern Analysis and Applications

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01-11-2015 | Theoretical advances

Evaluating cluster detection algorithms and feature extraction techniques in automatic classification of fish species

Authors: Marco T. A. Rodrigues, Mário H. G. Freitas, Flávio L. C. Pádua, Rogério M. Gomes, Eduardo G. Carrano

Published in: Pattern Analysis and Applications | Issue 4/2015

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Abstract

This paper proposes five different schemes for automatic classification of fish species. These schemes make the species recognition based on image sample analysis. Different techniques have been combined for building the classifiers: three feature extraction techniques (PCA, SIFT and SIFT + VLAD + PCA), three data clustering algorithms (aiNet, ARIA and k-means) and three input classifiers (k-NN, SIFT class. and k-means class) are tested. When compared to common methodologies, which are based on human observation, it is believed that these schemes are able to provide significant improvement in time and financial resources spent in classification. Two datasets have been considered: (1) a dataset with image samples of six fish species which are perfectly conserved in formaldehyde solution, and; (2) a dataset composed of images of four fish species in real-world conditions (in vivo). The five proposed schemes have been evaluated in both datasets, and a ranking for the methods has been derived for each one.

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Title: Evaluating cluster detection algorithms and feature extraction techniques in automatic classification of fish species
Authors: Marco T. A. Rodrigues
Mário H. G. Freitas
Flávio L. C. Pádua
Rogério M. Gomes
Eduardo G. Carrano
Publication date: 01-11-2015
Publisher: Springer London
Published in: Pattern Analysis and Applications / Issue 4/2015
Print ISSN: 1433-7541
Electronic ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-013-0362-6

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