scholarly journals Cloud Image Classification Method Based on Deep Convolutional Neural Network

2020 ◽  
Vol 38 (4) ◽  
pp. 740-746
Author(s):  
Fei Zhang ◽  
Jie Yan
Keyword(s):  
Image Classification ◽  
Transfer Learning ◽  
Training Model ◽  
Classification Method ◽  
Support Vector ◽  
Test Accuracy ◽  
Learning Technology ◽  
Deep Convolutional Neural Networks ◽  
Fitting In ◽  
The Cost

Compared with satellite remote sensing images, ground-based invisible images have limited swath, but featured in higher resolution, more distinct cloud features, and the cost is greatly reduced, conductive to continuous meteorological observation of local areas. For the first time, this paper proposed a high-resolution cloud image classification method based on deep learning and transfer learning technology for ground-based invisible images. Due to the limited amount of samples, traditional classifiers such as support vector machine can't effectively extract the unique features of different types of clouds, and directly training deep convolutional neural networks leads to over-fitting. In order to prevent the network from over-fitting, this paper proposed applying transfer learning method to fine-tune the pre-training model. The proposed network achieved as high as 85.19% test accuracy on 6-type cloud images classification task. The networks proposed in this paper can be applied to classify digital photos captured by cameras directly, which will reduce the cost of system greatly.

scholarly journals Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhe Xu ◽  
Xi Guo ◽  
Anfan Zhu ◽  
Xiaolin He ◽  
Xiaomin Zhao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Fine Tuning ◽  
Support Vector ◽  
Test Accuracy ◽  
Nutrient Deficiencies ◽  
Deep Convolutional Neural Networks ◽  
Histogram Of Oriented Gradient

Symptoms of nutrient deficiencies in rice plants often appear on the leaves. The leaf color and shape, therefore, can be used to diagnose nutrient deficiencies in rice. Image classification is an efficient and fast approach for this diagnosis task. Deep convolutional neural networks (DCNNs) have been proven to be effective in image classification, but their use to identify nutrient deficiencies in rice has received little attention. In the present study, we explore the accuracy of different DCNNs for diagnosis of nutrient deficiencies in rice. A total of 1818 photographs of plant leaves were obtained via hydroponic experiments to cover full nutrition and 10 classes of nutrient deficiencies. The photographs were divided into training, validation, and test sets in a 3 : 1 : 1 ratio. Fine-tuning was performed to evaluate four state-of-the-art DCNNs: Inception-v3, ResNet with 50 layers, NasNet-Large, and DenseNet with 121 layers. All the DCNNs obtained validation and test accuracies of over 90%, with DenseNet121 performing best (validation accuracy = 98.62 ± 0.57%; test accuracy = 97.44 ± 0.57%). The performance of the DCNNs was validated by comparison to color feature with support vector machine and histogram of oriented gradient with support vector machine. This study demonstrates that DCNNs provide an effective approach to diagnose nutrient deficiencies in rice.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

scholarly journals Multispectral Face Recognition Using Transfer Learning with Adaptation of Domain Specific Units

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4520
Author(s):  
Luis Lopes Chambino ◽  
José Silvestre Silva ◽  
Alexandre Bernardino
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Facial Recognition ◽  
Spectral Band ◽  
Support Vector ◽  
Multispectral Images ◽  
Forgery Detection ◽  
K Nearest Neighbor ◽  
Deep Convolutional Neural Networks ◽  
Domain Specific

Facial recognition is a method of identifying or authenticating the identity of people through their faces. Nowadays, facial recognition systems that use multispectral images achieve better results than those that use only visible spectral band images. In this work, a novel architecture for facial recognition that uses multiple deep convolutional neural networks and multispectral images is proposed. A domain-specific transfer-learning methodology applied to a deep neural network pre-trained in RGB images is shown to generalize well to the multispectral domain. We also propose a skin detector module for forgery detection. Several experiments were planned to assess the performance of our methods. First, we evaluate the performance of the forgery detection module using face masks and coverings of different materials. A second study was carried out with the objective of tuning the parameters of our domain-specific transfer-learning methodology, in particular which layers of the pre-trained network should be retrained to obtain good adaptation to multispectral images. A third study was conducted to evaluate the performance of support vector machines (SVM) and k-nearest neighbor classifiers using the embeddings obtained from the trained neural network. Finally, we compare the proposed method with other state-of-the-art approaches. The experimental results show performance improvements in the Tufts and CASIA NIR-VIS 2.0 multispectral databases, with a rank-1 score of 99.7% and 99.8%, respectively.

scholarly journals The classification of skateboarding tricks via transfer learning pipelines

2021 ◽  
Vol 7 ◽  
pp. e680
Author(s):  
Muhammad Amirul Abdullah ◽  
Muhammad Ar Rahim Ibrahim ◽  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Aizzat Zakaria ◽  
Mohd Azraai Mohd Razman ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Input Image ◽  
Computational Time ◽  
Support Vector ◽  
Svm Classifier ◽  
Test Accuracy ◽  
Learning Models ◽  
Test Dataset ◽  
Flat Ground

This study aims at classifying flat ground tricks, namely Ollie, Kickflip, Shove-it, Nollie and Frontside 180, through the identification of significant input image transformation on different transfer learning models with optimized Support Vector Machine (SVM) classifier. A total of six amateur skateboarders (20 ± 7 years of age with at least 5.0 years of experience) executed five tricks for each type of trick repeatedly on a customized ORY skateboard (IMU sensor fused) on a cemented ground. From the IMU data, a total of six raw signals extracted. A total of two input image type, namely raw data (RAW) and Continous Wavelet Transform (CWT), as well as six transfer learning models from three different families along with grid-searched optimized SVM, were investigated towards its efficacy in classifying the skateboarding tricks. It was shown from the study that RAW and CWT input images on MobileNet, MobileNetV2 and ResNet101 transfer learning models demonstrated the best test accuracy at 100% on the test dataset. Nonetheless, by evaluating the computational time amongst the best models, it was established that the CWT-MobileNet-Optimized SVM pipeline was found to be the best. It could be concluded that the proposed method is able to facilitate the judges as well as coaches in identifying skateboarding tricks execution.

scholarly journals Transfer learning for hostel image classification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chanattra Ammatmanee ◽  
Lu Gan
Keyword(s):  
Image Classification ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Image Database ◽  
Learning Ability ◽  
Learning Technology ◽  
Human Errors ◽  
Content Type ◽  
Training Time ◽  
Processing Steps

PurposeBecause of the fast-growing digital image collections on online platforms and the transfer learning ability of deep learning technology, image classification could be improved and implemented for the hostel domain, which has complex clusters of image contents. This paper aims to test the potential of 11 pretrained convolutional neural network (CNN) with transfer learning for hostel image classification on the first hostel image database to advance the knowledge and fill the gap academically, as well as to suggest an alternative solution in optimal image classification with less labour cost and human errors to those who manage hostel image collections.Design/methodology/approachThe hostel image database is first created with data pre-processing steps, data selection and data augmentation. Then, the systematic and comprehensive investigation is divided into seven experiments to test 11 pretrained CNNs which transfer learning was applied and parameters were fine-tuned to match this newly created hostel image dataset. All experiments were conducted in Google Colaboratory environment using PyTorch.FindingsThe 7,350 hostel image database is created and labelled into seven classes. Furthermore, its experiment results highlight that DenseNet 121 and DenseNet 201 have the greatest potential for hostel image classification as they outperform other CNNs in terms of accuracy and training time.Originality/valueThe fact that there is no existing academic work dedicating to test pretrained CNNs with transfer learning for hostel image classification and no existing hostel image-only database have made this paper a novel contribution.

scholarly journals A Hybrid Classification Method of Medical Image Based on Deep Learning

2021 ◽  
Author(s):  
Yulong Wang ◽  
Xiaofeng Liao ◽  
Dewen Qiao ◽  
Jiahui Wu
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Medical Image ◽  
Rapid Development ◽  
Medical Science ◽  
Image Features ◽  
Image Feature ◽  
Classification Method ◽  
Support Vector ◽  
Medical Image Classification

Abstract With the rapid development of modern medical science and technology, medical image classification has become a more and more challenging problem. However, in most traditional classification methods, image feature extraction is difficult, and the accuracy of classifier needs to be improved. Therefore, this paper proposes a high-accuracy medical image classification method based on deep learning, which is called hybrid CQ-SVM. Specifically, we combine the advantages of convolutional neural network (CNN) and support vector machine (SVM), and integrate the novel hybrid model. In our scheme, quantum-behaved particle swarm optimization algorithm (QPSO) is adopted to set its parameters automatically for solving the SVM parameter setting problem, CNN works as a trainable feature extractor and SVM optimized by QPSO performs as a trainable classifier. This method can automatically extract features from original medical images and generate predictions. The experimental results show that this method can extract better medical image features, and achieve higher classification accuracy.

scholarly journals Aerial Scene Classification through Fine-Tuning with Adaptive Learning Rates and Label Smoothing

2020 ◽  
Vol 10 (17) ◽  
pp. 5792 ◽  
Author(s):  
Biserka Petrovska ◽  
Tatjana Atanasova-Pacemska ◽  
Roberto Corizzo ◽  
Paolo Mignone ◽  
Petre Lameski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Feature Extraction ◽  
Image Classification ◽  
Transfer Learning ◽  
Remote Sensing Image ◽  
Learning Rate ◽  
Fine Tuning ◽  
Support Vector ◽  
Learning Rates

Remote Sensing (RS) image classification has recently attracted great attention for its application in different tasks, including environmental monitoring, battlefield surveillance, and geospatial object detection. The best practices for these tasks often involve transfer learning from pre-trained Convolutional Neural Networks (CNNs). A common approach in the literature is employing CNNs for feature extraction, and subsequently train classifiers exploiting such features. In this paper, we propose the adoption of transfer learning by fine-tuning pre-trained CNNs for end-to-end aerial image classification. Our approach performs feature extraction from the fine-tuned neural networks and remote sensing image classification with a Support Vector Machine (SVM) model with linear and Radial Basis Function (RBF) kernels. To tune the learning rate hyperparameter, we employ a linear decay learning rate scheduler as well as cyclical learning rates. Moreover, in order to mitigate the overfitting problem of pre-trained models, we apply label smoothing regularization. For the fine-tuning and feature extraction process, we adopt the Inception-v3 and Xception inception-based CNNs, as well the residual-based networks ResNet50 and DenseNet121. We present extensive experiments on two real-world remote sensing image datasets: AID and NWPU-RESISC45. The results show that the proposed method exhibits classification accuracy of up to 98%, outperforming other state-of-the-art methods.

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