An SVM-Based AdaBoost Cascade Classifier for Sonar Image

This paper proposes an improved AdaBoost classifier for sonar images with low resolution ratio and noise. First, Histogram of oriented gradient (HOG) is used to perform feature extraction, and a weak classifier is obtained by Support vector machine (SVM) at the same time. Then, multiple SVM models a...
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Autor*in:	Huipu Xu [verfasserIn] Haiyan Yuan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	AdaBoost sonar image classification SVM

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 115857-115864
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:115857-115864

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.3004473

Katalog-ID:	DOAJ069596840

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spelling	10.1109/ACCESS.2020.3004473 doi (DE-627)DOAJ069596840 (DE-599)DOAJfaf5aec89ec34191877e359dd8aa0858 DE-627 ger DE-627 rakwb eng TK1-9971 Huipu Xu verfasserin aut An SVM-Based AdaBoost Cascade Classifier for Sonar Image 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes an improved AdaBoost classifier for sonar images with low resolution ratio and noise. First, Histogram of oriented gradient (HOG) is used to perform feature extraction, and a weak classifier is obtained by Support vector machine (SVM) at the same time. Then, multiple SVM models are constructed for target classification based on the AdaBoost cascade classification framework. A new function for updating sample weights has been designed in this paper to improve the accuracy of the classifier. And new iteration rules of classifier have been made to reduce the training time of the proposed method. The experimental results on the sonar dataset which are proposed for improving the generalization ability in this paper show that the classification accuracy of the proposed algorithm is about 92%, and the accuracy on Cifar-10 dataset is also higher than general methods. AdaBoost sonar image classification SVM Electrical engineering. Electronics. Nuclear engineering Haiyan Yuan verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 115857-115864 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:115857-115864 https://doi.org/10.1109/ACCESS.2020.3004473 kostenfrei https://doaj.org/article/faf5aec89ec34191877e359dd8aa0858 kostenfrei https://ieeexplore.ieee.org/document/9123331/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 115857-115864
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allfieldsSound	10.1109/ACCESS.2020.3004473 doi (DE-627)DOAJ069596840 (DE-599)DOAJfaf5aec89ec34191877e359dd8aa0858 DE-627 ger DE-627 rakwb eng TK1-9971 Huipu Xu verfasserin aut An SVM-Based AdaBoost Cascade Classifier for Sonar Image 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes an improved AdaBoost classifier for sonar images with low resolution ratio and noise. First, Histogram of oriented gradient (HOG) is used to perform feature extraction, and a weak classifier is obtained by Support vector machine (SVM) at the same time. Then, multiple SVM models are constructed for target classification based on the AdaBoost cascade classification framework. A new function for updating sample weights has been designed in this paper to improve the accuracy of the classifier. And new iteration rules of classifier have been made to reduce the training time of the proposed method. The experimental results on the sonar dataset which are proposed for improving the generalization ability in this paper show that the classification accuracy of the proposed algorithm is about 92%, and the accuracy on Cifar-10 dataset is also higher than general methods. AdaBoost sonar image classification SVM Electrical engineering. Electronics. Nuclear engineering Haiyan Yuan verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 115857-115864 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:115857-115864 https://doi.org/10.1109/ACCESS.2020.3004473 kostenfrei https://doaj.org/article/faf5aec89ec34191877e359dd8aa0858 kostenfrei https://ieeexplore.ieee.org/document/9123331/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 115857-115864
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source	In IEEE Access 8(2020), Seite 115857-115864 volume:8 year:2020 pages:115857-115864
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callnumber-first	T - Technology
author	Huipu Xu
spellingShingle	Huipu Xu misc TK1-9971 misc AdaBoost misc sonar image classification misc SVM misc Electrical engineering. Electronics. Nuclear engineering An SVM-Based AdaBoost Cascade Classifier for Sonar Image
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topic_title	TK1-9971 An SVM-Based AdaBoost Cascade Classifier for Sonar Image AdaBoost sonar image classification SVM
topic	misc TK1-9971 misc AdaBoost misc sonar image classification misc SVM misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc AdaBoost misc sonar image classification misc SVM misc Electrical engineering. Electronics. Nuclear engineering
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title	An SVM-Based AdaBoost Cascade Classifier for Sonar Image
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title_full	An SVM-Based AdaBoost Cascade Classifier for Sonar Image
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title_sort	svm-based adaboost cascade classifier for sonar image
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title_auth	An SVM-Based AdaBoost Cascade Classifier for Sonar Image
abstract	This paper proposes an improved AdaBoost classifier for sonar images with low resolution ratio and noise. First, Histogram of oriented gradient (HOG) is used to perform feature extraction, and a weak classifier is obtained by Support vector machine (SVM) at the same time. Then, multiple SVM models are constructed for target classification based on the AdaBoost cascade classification framework. A new function for updating sample weights has been designed in this paper to improve the accuracy of the classifier. And new iteration rules of classifier have been made to reduce the training time of the proposed method. The experimental results on the sonar dataset which are proposed for improving the generalization ability in this paper show that the classification accuracy of the proposed algorithm is about 92%, and the accuracy on Cifar-10 dataset is also higher than general methods.
abstractGer	This paper proposes an improved AdaBoost classifier for sonar images with low resolution ratio and noise. First, Histogram of oriented gradient (HOG) is used to perform feature extraction, and a weak classifier is obtained by Support vector machine (SVM) at the same time. Then, multiple SVM models are constructed for target classification based on the AdaBoost cascade classification framework. A new function for updating sample weights has been designed in this paper to improve the accuracy of the classifier. And new iteration rules of classifier have been made to reduce the training time of the proposed method. The experimental results on the sonar dataset which are proposed for improving the generalization ability in this paper show that the classification accuracy of the proposed algorithm is about 92%, and the accuracy on Cifar-10 dataset is also higher than general methods.
abstract_unstemmed	This paper proposes an improved AdaBoost classifier for sonar images with low resolution ratio and noise. First, Histogram of oriented gradient (HOG) is used to perform feature extraction, and a weak classifier is obtained by Support vector machine (SVM) at the same time. Then, multiple SVM models are constructed for target classification based on the AdaBoost cascade classification framework. A new function for updating sample weights has been designed in this paper to improve the accuracy of the classifier. And new iteration rules of classifier have been made to reduce the training time of the proposed method. The experimental results on the sonar dataset which are proposed for improving the generalization ability in this paper show that the classification accuracy of the proposed algorithm is about 92%, and the accuracy on Cifar-10 dataset is also higher than general methods.
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title_short	An SVM-Based AdaBoost Cascade Classifier for Sonar Image
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