An SVM-Based AdaBoost Cascade Classifier for Sonar Image

Xu, Huipu; Yuan, Hongliang

doi:10.1109/access.2020.3004473

Cited by 13 publications

(8 citation statements)

References 23 publications

(21 reference statements)

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“…A combined SVM and AdaBoost approach for sonar image classification was presented in Ref. 22. All of the above methods are based on traditional machine learning methods for sonar image classification.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised classification based on deep adaptation network for sonar images

Yang

Zhang

2023

J. Electron. Imag.

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.The research of underwater sonar image classification is beneficial to the development of marine resources. In recent years, deep neural networks have achieved great success in the fields of image classification and image recognition. However, deep neural networks require a large amount of training data. Due to the difficulty of obtaining sonar image datasets, the existing public sonar datasets are generally small, and sonar image classification can be regarded as a small sample problem. To solve this problem, we designed a deep adaptive sonar image classification network (DASCN) based on deep learning and domain adaptation. The feature extraction module in DASCN extracts multiscale features of images; the attention module learns the importance of different channel features; and the domain adaptation module reduces the difference between the source domain and the target domain. It is worth noting that our DASCN does not require a large number of training samples, and sonar images used for training do not need to be labeled. As demonstrated in comprehensive experiments, the classification accuracy of the DASCN model reached 89.4% on the sonar image dataset. Our DASCN achieves unsupervised accurate classification of small sample sonar images. In addition, our DASCN has good classification results on the Office-31 dataset and the Office Home dataset and has good generalization performance.

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Section: Related Workmentioning

confidence: 99%

Unsupervised classification based on deep adaptation network for sonar images

Yang

Zhang

2023

J. Electron. Imag.

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“…Support Vector Machine(SVM) is a popular supervised learning approach used for classification. Multiple SVM models [5] can be constructed for image classification based on AdaBoost framework. CNN's also found their applications in medical fields [6] like identifying the damaged retina of eye caused due to diabetes.…”

Section: Related Workmentioning

confidence: 99%

Calculating Screen Time of Characters in a Video Using Convolutional Neural Networks

2020

IJATCSE

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In this growing world of digital technology, images and videos are one of the richest sources of data on the internet. Several technologies have emerged which help the users to deal with the image and video data. In this paper, we propose a methodology to calculate the screen time of characters appearing in the video by using the classification-count method.Supervised Learning Methodology with Convolutional Neural Network as the classifier is used in this paper. The proposed methodology is implemented on a series of Tom and Jerry videos available publicly on the internet. Experimental results show that the proposed methodology has shown good accuracy.

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“…Such problems have certain limitations in practical applications [7]. In recent years, with the continuous development of artificial intelligence technology, diagnostic methods have also developed from the traditional IEC three-ratio method and improved three-ratio method to machine learning and other artificial intelligence methods, such as neural networks, Support Vector Machines (SVM), Fuzzy Algorithms, Bayesian theory, Normal cloud model [8][9][10][11][12][13], etc. Although these methods have achieved specific diagnostic effects, they have also solved some of the problems of traditional algorithm boundaries that are too absolute and easy to overfit.…”

Section: Introductionmentioning

confidence: 99%

Transformer Fault Diagnosis Based on Multi-Class AdaBoost Algorithm

Hai

et al. 2022

IEEE Access

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Traditional shallow machine learning algorithms cannot effectively explore the relationship between the fault data of oil-immersed transformers, resulting in low fault diagnosis accuracy. This paper proposes a transformer fault diagnosis method based on Multi-class AdaBoost Algorithms in response to this problem. First, the AdaBoost algorithm is combined with Support Vector Machines (SVM), The SVM is enhanced through the AdaBoost algorithm, and the transformer fault data is deeply explored. Then the dynamic weight is introduced into the Particle Swarm Optimization (PSO); through the real-time update of the particle inertia weight, the search accuracy and optimization speed of the particle swarm optimization algorithm is improved, and the improved particle swarm optimization algorithm (IPSO) is used to optimize the parameters of the SVM. Finally, by analyzing the relationship between the dissolved gas in the transformer oil and the fault type, the uncoded ratio method is used to form a new gas group cooperation, and the improved ratio method is constructed as the input feature vector. Simulations based on 117 sets of IECTC10 standard data and 419 sets of transformer fault data collected in China show that the diagnosis method proposed in this paper not only has strong search ability and fast convergence speed but also has a significant improvement in diagnostic accuracy compared with traditional methods.

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An SVM-Based AdaBoost Cascade Classifier for Sonar Image

Cited by 13 publications

References 23 publications

Unsupervised classification based on deep adaptation network for sonar images

Unsupervised classification based on deep adaptation network for sonar images

Calculating Screen Time of Characters in a Video Using Convolutional Neural Networks

Transformer Fault Diagnosis Based on Multi-Class AdaBoost Algorithm

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