A new image classification method using CNN transfer learning and web data augmentation

2018 ◽  
Vol 95 ◽  
pp. 43-56 ◽  
Author(s):  
Dongmei Han ◽  
Qigang Liu ◽  
Weiguo Fan
Keyword(s):  
Image Classification ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Classification Method ◽  
Web Data

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 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.

Data Augmentation and Transfer Learning Applied to Charcoal Image Classification

2019 ◽  
Author(s):  
Luciana T. Menon ◽  
Israel A. Laurensi ◽  
Manoel C. Penna ◽  
Luiz E. S. Oliveira ◽  
Alceu S. Britto
Keyword(s):  
Image Classification ◽  
Transfer Learning ◽  
Data Augmentation

scholarly journals Image Classification using ImageNet Classifiers in Environments with Limited Data

2021 ◽  
Author(s):  
Anirvin Sharma ◽  
Abhinav Singh ◽  
Tanupriya Choudhury ◽  
Tanmay Sarkar
Keyword(s):  
Image Classification ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Classification Algorithms ◽  
Limited Data ◽  
Training Time ◽  
Learning Framework ◽  
Rare Phenomena ◽  
Fully Connected ◽  
Compare And Contrast

Abstract In this research, we compare and contrast various image classification algorithms and how effective they are in specific problem sets where data might be scarce such as prediction of rare phenomena (for example, natural calamities), enterprise solutions etc. We have employed various state-of-the-art algorithms in this study credited to have been some of the best classifiers at the time of their inception. These classifiers have also been suspected to fall prey to overfitting on the datasets they were initially tested on viz. ImageNet and Common Objects in Context (COCO); we test to what extent these classifiers tend to generalize to the new data provided by us in a transfer learning framework. We utilize transfer learning on the ImageNet classifiers to adapt to our smaller dataset and examine various techniques such as data augmentation, batch normalization, dropout etc. to mitigate overfitting. All the classifiers follow a standard fully connected architecture. The end result should provide the reader with an overall analysis of which algorithm or approach to use in conditions where data might be limited while also giving a brief overview of the progress of image classification algorithms since their advent. We also provide an analysis on the effectiveness of data augmentation in limited datasets by providing results achieved with and without utilizing data augmentation. In our case, we found the MobileNet (with its lightweight nature contributing to low computational costs) and InceptionV3 (owing to its lower training time) to be the best performing classifiers for applying transfer learning to limited datasets out of the classifiers we have used for our study. This paper aims to establish preemptive standards that can be used to evaluate the models which can be used in object recognition, and image classification for problems containing limited amounts of data.

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