Automatic Generation of Training Data for Image Classification of Road Scenes

2019 ◽  
Author(s):  
Tilman Kuhner ◽  
Sascha Wirges ◽  
Martin Lauer
Keyword(s):  
Image Classification ◽  
Automatic Generation ◽  
Training Data

scholarly journals Automatic Generation Of Training Data For Hyperspectral Image Classification Using Support Vector Machine

2015 ◽  
Vol XL-7/W3 ◽  
pp. 575-580 ◽  
Author(s):  
B. Abbasi ◽  
H. Arefi ◽  
B. Bigdeli ◽  
S. Roessner
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Satellite Image ◽  
Automatic Generation ◽  
Training Data ◽  
Experimental Result ◽  
Support Vector ◽  
Feature Descriptors

An image classification method based on Support Vector Machine (SVM) is proposed on hyperspectral and 3K DSM data. To obtain training data we applied an automatic method relating to four classes namely; building, grass, tree, and ground pixels. First, some initial segments regarding to building, tree, grass, and ground pixels are produced using different feature descriptors. The feature descriptors are generated using optical (hyperspectral) as well as range (3K DSM) images. The initial building regions are created using DSM segmentation. Fusion of NDVI and elevation information assist us to provide initial segments regarding to the grass and tree areas. Also, we created initial segment regarding to ground pixel after geodesic based filtering of DSM and elimination of the non-ground pixels. To improve classification accuracy, the hyperspectral image and 3K DSM were utilized simultaneously to perform image classification. For obtaining testing data, labelled pixels was divide into two parts: test and training. Experimental result shows a final classification accuracy of about 90% using Support Vector Machine. In the process of satellite image classification; provided by 3K camera. Both datasets correspond to Munich area in Germany.

Automated image classification of outlet glaciers in Greenland using deep learning

2020 ◽  
Author(s):  
Melanie Marochov ◽  
Patrice Carbonneau ◽  
Chris Stokes
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Classification ◽  
Supervised Classification ◽  
Greenland Ice Sheet ◽  
Training Data ◽  
Glacier Change ◽  
Outlet Glacier ◽  
Glacial Environments

<p>In recent decades, a wealth of research has focused on elucidating the key controls on the mass loss of the Greenland Ice Sheet and its response to climate forcing, specifically in relation to the drivers of spatio-temporally variable outlet glacier change. Despite the increasing availability of high-resolution satellite data, the time-consuming nature of the manual methods traditionally used to analyse satellite imagery has resulted in a significant bottleneck in the monitoring of outlet glacier change. Recent advances in deep learning applied to image processing have opened up a new frontier in the area of automated delineation of glacier termini. However, at this stage, there remains a paucity of research on the use of deep learning for image classification of outlet glacier landscapes. In this contribution, we apply a deep learning approach based on transfer learning to automatically classify satellite images of Helheim glacier, the fastest flowing outlet glacier in eastern Greenland. The method uses the well-established VGG16 convolutional neural network (CNN), and is trained on 224x224 pixel tiles derived from Sentinel-2 RGB bands, which have a spatial resolution of 10 metres. Based on features learned from ImageNet and limited training data, our deep learning model can classify glacial environments with >85% accuracy. In future stages of this research, we will use a new method originally developed for fluvial settings, dubbed ‘CNN-Supervised Classification’ (CSC). CSC uses a pre-trained CNN (in this case our VGG16 model) to replace the human operator’s role in traditional supervised classification by automatically producing new label data to train a pixel-level neural network classifier for any new image. This transferable approach to image classification of outlet glacier landscapes permits not only automated terminus delineation, but also facilitates the efficient analysis of numerous processes controlling outlet glacier behaviour, such as fjord geometry, subglacial plumes, and supra-glacial lakes.</p>

scholarly journals Klasifikasi Citra Histopatologi Kanker Payudara menggunakan Data Resampling Random dan Residual Network

2021 ◽  
Vol 11 (1) ◽  
pp. 70-77
Author(s):  
Wahyudi Setiawan
Keyword(s):  
Breast Cancer ◽  
Image Classification ◽  
Training Data ◽  
Unbalanced Data ◽  
Residual Network ◽  
Average Precision ◽  
Average Recall ◽  
Average Accuracy ◽  
Optimal Classification

Data imbalance between classes is one of the problems on image classification. The data in each class is not equal and has a relatively large difference generated in less than optimal classification results. Ideally, the data in each class is equal or have a slight difference. This article discusses the classification of the histopathology breast cancer image. The data consist of  8 classes with unbalanced data. The method for balancing the data in each class uses random resampling which is applied to training data only. The data used from BreakHist with magnifications of 40x, 100x, 200x, and 400x . The classification uses Residual Network (ResNet) 18 and 50. The best results are obtained on images with a magnification of 400x. Classification results using ResNet18 has an average accuracy of 79.82%, an average precision of 71.39%, and an average recall of 82.37%. Meanwhile using ResNet50 showed an average accuracy of 81.67%, average precision of 78.41%, and an average recall of 82.91%.

scholarly journals Classification of Similar Sports Images Using Convolutional Neural Network with Hyper-Parameter Optimization

2020 ◽  
Vol 10 (23) ◽  
pp. 8494
Author(s):  
Vili Podgorelec ◽  
Špela Pečnik ◽  
Grega Vrbančič
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Parameter Optimization ◽  
Classification Performance ◽  
Training Data ◽  
Fine Tuning ◽  
American Football ◽  
Massive Scale

With the exponential growth of the presence of sport in the media, the need for effective classification of sports images has become crucial. The traditional approaches require carefully hand-crafted features, which make them impractical for massive-scale data and less accurate in distinguishing images that are very similar in appearance. As the deep learning methods can automatically extract deep representation of training data and have achieved impressive performance in image classification, our goal was to apply them to automatic classification of very similar sports disciplines. For this purpose, we developed a CNN-TL-DE method for image classification using the fine-tuning of transfer learning for training a convolutional neural network model with the use of hyper-parameter optimization based on differential evolution. Through the automatic optimization of neural network topology and essential training parameters, we significantly improved the classification performance evaluated on a dataset composed from images of four similar sports—American football, rugby, soccer, and field hockey. The analysis of interpretable representation of the trained model additionally revealed interesting insights into how our model perceives images which contributed to a greater confidence in the model prediction. The performed experiments showed our proposed method to be a very competitive image classification method for distinguishing very similar sports and sport situations.

scholarly journals Improving Hierarchical Decision Approach for Single Image Classification of Pap Smear

2018 ◽  
Vol 8 (6) ◽  
pp. 5415
Author(s):  
Dwiza Riana ◽  
Yudi Ramdhani ◽  
Rizki Tri Prasetio ◽  
Achmad Nizar Hidayanto
Keyword(s):  
Genetic Algorithm ◽  
Image Classification ◽  
Pap Smear ◽  
Learning Rate ◽  
Training Data ◽  
Pap Smears ◽  
Single Image ◽  
New Model ◽  
Hierarchical Decision

The single image classification of Pap smears is an important part of the early detection of cervical cancer through Pap smear tests. Unfortunately, most classification processes still require accuracy enhancement, especially to complete the classification in seven classes and to get a qualified classification process. In addition, attempts to improve the single image classification of Pap smears were performed to be able to distinguish normal and abnormal cells. This study proposes a better approach by providing different handling of the initial data preparation process in the form of the distribution for training data and testing data so that it resulted in a new model of Hierarchial Decision Approach (HDA) which has the higher learning rate and momentum values in the proposed new model. This study evaluated 20 different features in hierarchical decision approach model based on Neural Network (NN) and genetic algorithm method for single image classification of Pap smear which resulted in classification experiment using value learning rate of 0.3 and momentum of 0.2 and value of learning rate of 0.5 and momentum of 0.5 by generating classification of 7 classes (Normal Intermediate, Normal Colummar, Mild (Light) Dyplasia, Moderate Dyplasia, Servere Dyplasia and Carcinoma In Situ) better. The accuracy value enhancemenet were also influenced by the application of Genetic Algorithm to feature selection. Thus, from the results of model testing, it can be concluded that the Hierarchical Decision Approach (HDA) method for Pap Smear image classification can be used as a reference for initial screening process to analyze Pap Smear image classification.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

scholarly journals An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 249
Author(s):  
Xin Jin ◽  
Yuanwen Zou ◽  
Zhongbing Huang
Keyword(s):  
Cell Cycle ◽  
Deep Learning ◽  
Image Classification ◽  
Classification Accuracy ◽  
Data Augmentation ◽  
Cycle Phase ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

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