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A Novel Classification Method of Fish Based on Multi-Feature Fusion

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Abstract:

In order to solve fish classification problems based on acoustic scattering data, temporal centroid (TC) features and discrete cosine transform (DCT) coefficients features used to analyze acoustic scattering characteristics of fish from different aspects are extracted. The extracted features of fish are reduced in dimension and fused, and support vector machine (SVM) classifier is used to classify and identify the fishes. Three kinds of different fishes are selected as research objects in this paper, the correct identification rates are given based on temporal centroid features and discrete cosine transform coefficients features and fused features. The processing results of actual experimental data show that multi-feature fusion method can improve the identification rate at about 5% effectively.

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Periodical:

Applied Mechanics and Materials (Volumes 713-715)

Pages:

1513-1519

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.713-715.1513

Citation:

Cite this paper

Online since:

January 2015

Authors:

Wei Dong Du*, Bao Wei Chen, Hai Sen Li, Chao Xu

Keywords:

Discrete Cosine Transform, Features Fusion, Fish Classification, Temporal Centroid

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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