scholarly journals Ship Classification Based on Multifeature Ensemble with Convolutional Neural Network

2019 ◽  
Vol 11 (4) ◽  
pp. 419 ◽  
Author(s):  
Qiaoqiao Shi ◽  
Wei Li ◽  
Ran Tao ◽  
Xu Sun ◽  
Lianru Gao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Fractional Fourier Transform ◽  
Gabor Filter ◽  
Feature Learning ◽  
Feature Representation ◽  
Optical Remote Sensing ◽  
Discriminative Feature ◽  
Ship Classification ◽  
High Level

As an important part of maritime traffic, ships play an important role in military and civilian applications. However, ships’ appearances are susceptible to some factors such as lighting, occlusion, and sea state, making ship classification more challenging. This is of great importance when exploring global and detailed information for ship classification in optical remote sensing images. In this paper, a novel method to obtain discriminative feature representation of a ship image is proposed. The proposed classification framework consists of a multifeature ensemble based on convolutional neural network (ME-CNN). Specifically, two-dimensional discrete fractional Fourier transform (2D-DFrFT) is employed to extract multi-order amplitude and phase information, which contains such important information as profiles, edges, and corners; completed local binary pattern (CLBP) is used to obtain local information about ship images; Gabor filter is used to gain the global information about ship images. Then, deep convolutional neural network (CNN) is applied to extract more abstract features based on the above information. CNN, extracting high-level features automatically, has performed well for object classification tasks. After high-feature learning, as the one of fusion strategies, decision-level fusion is investigated for the final classification result. The average accuracy of the proposed approach is 98.75% on the BCCT200-resize data, 92.50% on the original BCCT200 data, and 87.33% on the challenging VAIS data, which validates the effectiveness of the proposed method when compared to the existing state-of-art algorithms.

scholarly journals Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images

2019 ◽  
Vol 9 (20) ◽  
pp. 4209 ◽  
Author(s):  
Yongmei Ren ◽  
Jie Yang ◽  
Qingnian Zhang ◽  
Zhiqiang Guo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Feature Fusion ◽  
Structural Information ◽  
Confusion Matrix ◽  
Weather Conditions ◽  
Local Binary Patterns ◽  
Feature Representation ◽  
Ship Classification ◽  
High Level

The appearance of ships is easily affected by external factors—illumination, weather conditions, and sea state—that make ship classification a challenging task. To facilitate realization of enhanced ship-classification performance, this study proposes a ship classification method based on multi-feature fusion with a convolutional neural network (CNN). First, an improved CNN characterized by shallow layers and few parameters is proposed to learn high-level features and capture structural information. Second, handcrafted features of the histogram of oriented gradients (HOG) and local binary patterns (LBP) are combined with high-level features extracted by the improved CNN in the last fully connected layer to obtain discriminative feature representation. The handcrafted features supplement the edge information and spatial texture information of the ship images. Then, the Softmax function is used to classify different types of ships in the output layer. Effectiveness of the proposed method is evaluated based on its application to two datasets—one self-built and the other publicly available, called visible and infrared spectrums (VAIS). As observed, the proposed method demonstrated attainment of average classification accuracies equal to 97.50% and 93.60%, respectively, when applied to these datasets. Additionally, results obtained in terms of the F1-score and confusion matrix demonstrate the proposed method to be superior to some state-of-the-art methods.

Person Re-Identification Based on Convolutional Neural Network Discriminative Feature Learning

2018 ◽  
Vol 38 (7) ◽  
pp. 0720001 ◽  
Author(s):  
陈兵 Chen Bing ◽  
查宇飞 Zha Yufei ◽  
李运强 Li Yunqiang ◽  
张胜杰 Zhang Shengjie ◽  
张园强 Zhang Yuanqiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Feature Learning ◽  
Discriminative Feature

scholarly journals Ship Classification Based on Attention Mechanism and Multi-Scale Convolutional Neural Network for Visible and Infrared Images

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2022
Author(s):  
Yongmei Ren ◽  
Jie Yang ◽  
Zhiqiang Guo ◽  
Qingnian Zhang ◽  
Hui Cao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Classification Performance ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Infrared Images ◽  
Visible Image ◽  
Multi Scale ◽  
Ship Classification ◽  
Fusion Features

Visible image quality is very susceptible to changes in illumination, and there are limitations in ship classification using images acquired by a single sensor. This study proposes a ship classification method based on an attention mechanism and multi-scale convolutional neural network (MSCNN) for visible and infrared images. First, the features of visible and infrared images are extracted by a two-stream symmetric multi-scale convolutional neural network module, and then concatenated to make full use of the complementary features present in multi-modal images. After that, the attention mechanism is applied to the concatenated fusion features to emphasize local details areas in the feature map, aiming to further improve feature representation capability of the model. Lastly, attention weights and the original concatenated fusion features are added element by element and fed into fully connected layers and Softmax output layer for final classification output. Effectiveness of the proposed method is verified on a visible and infrared spectra (VAIS) dataset, which shows 93.81% accuracy in classification results. Compared with other state-of-the-art methods, the proposed method could extract features more effectively and has better overall classification performance.

scholarly journals Discriminative Feature Learning for Skin Disease Classification Using Deep Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 39025-39033 ◽  
Author(s):  
Belal Ahmad ◽  
Mohd Usama ◽  
Chuen-Min Huang ◽  
Kai Hwang ◽  
M. Shamim Hossain ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Skin Disease ◽  
Feature Learning ◽  
Deep Convolutional Neural Network ◽  
Disease Classification ◽  
Discriminative Feature

scholarly journals A Novel Region-Extreme Convolutional Neural Network for Melanoma Malignancy Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Nudrat Nida ◽  
Aun Irtaza ◽  
Muhammad Haroon Yousaf
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Medical Image Analysis ◽  
Feature Representation ◽  
Machine Learning Techniques ◽  
Feature Maps ◽  
Model Learning ◽  
Learning Machine ◽  
Elm Classifier ◽  
High Level

Melanoma malignancy recognition is a challenging task due to the existence of intraclass similarity, natural or clinical artefacts, skin contrast variation, and higher visual similarity among the normal or melanoma-affected skin. To overcome these problems, we propose a novel solution by leveraging “region-extreme convolutional neural network” for melanoma malignancy recognition as malignant or benign. Recent works on melanoma malignancy recognition employed the traditional machine learning techniques based on various handcrafted features or the recently introduced CNN network. However, the efficient training of these models is possible, if they localize the melanoma affected region and learn high-level feature representation from melanoma lesion to predict melanoma malignancy. In this paper, we incorporate this observation and propose a novel “region-extreme convolutional neural network” for melanoma malignancy recognition. Our proposed region-extreme convolutional neural network refines dermoscopy images to eliminate natural or clinical artefacts, localizes melanoma affected region, and defines precise boundary around the melanoma lesion. The defined melanoma lesion is used to generate deep feature maps for model learning using the extreme learning machine (ELM) classifier. The proposed model is evaluated on two challenge datasets (ISIC-2016 and ISIC-2017) and performs better than ISIC challenge winners. Our region-extreme convolutional neural network recognizes the melanoma malignancy 85% on ISIC-2016 and 93% on ISIC-2017 datasets. Our region-extreme convolutional neural network precisely segments the melanoma lesion with an average Jaccard index of 0.93 and Dice score of 0.94. The region-extreme convolutional neural network has several advantages: it eliminates the clinical and natural artefacts from dermoscopic images, precisely localizes and segments the melanoma lesion, and improves the melanoma malignancy recognition through feedforward model learning. The region-extreme convolutional neural network achieves significant performance improvement over existing methods that makes it adaptable for solving complex medical image analysis problems.

scholarly journals D-dCNN: A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5286 ◽  
Author(s):  
Ugochukwu Ejike Akpudo ◽  
Jang-Wook Hur
Keyword(s):  
Neural Network ◽  
Feature Learning ◽  
Ease Of Use ◽  
Fault Detection And Isolation ◽  
Vibration Signals ◽  
One Dimensional ◽  
Real Time Applications ◽  
Equipment State ◽  
High Level

This paper develops a novel hybrid feature learner and classifier for vibration-based fault detection and isolation (FDI) of industrial apartments. The trained model extracts high-level discriminative features from vibration signals and predicts equipment state. Against the limitations of traditional machine learning (ML)-based classifiers, the convolutional neural network (CNN) and deep neural network (DNN) are not only superior for real-time applications, but they also come with other benefits including ease-of-use, automated feature learning, and higher predictive accuracies. This study proposes a hybrid DNN and one-dimensional CNN diagnostics model (D-dCNN) which automatically extracts high-level discriminative features from vibration signals for FDI. Via Softmax averaging at the output layer, the model mitigates the limitations of the standalone classifiers. A diagnostic case study demonstrates the efficiency of the model with a significant accuracy of 92% (F1 score) and extensive comparative empirical validations.

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