Automated approach to classification of mine-like objects using multiple-aspect sonar images

• Autorzy: Wang X. , Liu X. , Japkowicz N. , Matwin S.

Identyfikatory

DOI

10.1515/jaiscr-2015-0004

• Języki publikacji: EN

Abstrakty

EN

In this paper, the detection of mines or other objects on the seabed from multiple side-scan sonar views is considered. Two frameworks are provided for this kind of classification. The first framework is based upon the Dempster–Shafer (DS) concept of fusion from a single-view kernel-based classifier and the second framework is based upon the concepts of multi-instance classifiers. Moreover, we consider the class imbalance problem which is always presents in sonar image recognition. Our experimental results show that both of the presented frameworks can be used in mine-like object classification and the presented methods for multi-instance class imbalanced problem are also effective in such classification.

Słowa kluczowe

EN

object; sea bed; multiple side-scan sonar; Dempster-Shafer theory; DS concept; classifier; imbalance; imbalanced problem; multi-instance class

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2014
• Tom: Vol. 4, No. 2
• Strony: 133--148
• Opis fizyczny: Bibliogr. 49 poz., rys.

Twórcy

autor

Wang X.

• Faculty of Computer Science Dalhousie University, Canada

autor

Liu X.

• Faculty of Computer Science Dalhousie University, Canada

autor

Japkowicz N.

• School of Electrical Engineering & Computer Science University of Ottawa, Canada

autor

Matwin S.

• Faculty of Computer Science, Dalhousie University, Canada Institute of Computer Science, Polish Academy of Sciences, Poland

Bibliografia

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