scholarly journals One-dimensional DCNN Feature Selective Transformation with LSTM-RDN for Image Classification

2021 ◽  
Author(s):  
li chaorong ◽  
Yuanyuan Huang ◽  
WEI HUANG ◽  
Fengqing Qin
Keyword(s):  
Feature Selection ◽  
Image Classification ◽  
Large Scale ◽  
Short Term Memory ◽  
Recognition Performance ◽  
Classification Method ◽  
Deep Convolutional Neural Networks ◽  
One Dimensional ◽  
Selective Transformation ◽  
Image Datasets

Feature selection and transformation are the important techniques in machine learning field. A good feature selection or transformation will greatly improve the performance of classification method. In this work, we proposed a simple but efficient image classification method which is based on two-stage processing strategy. In the first stage, the one-dimensional features are obtained from image by transfer learning with the pre-trained Deep Convolutional Neural Networks (DCNN). These one-dimensional DCNN features still have the shortcomings of information redundancy and weak distinguishing ability. Therefore, it is necessary to use feature transformation to further obtain more discriminative features. We propose a feature learning and selective transformation network based on Long Short-Term Memory (LSTM) combing ReLU and Dropout layers (called LSTM-RDN) to further process one-dimensional DCNN features. The verification experiments were conducted on three public object image datasets (Cifar10, Cifar100 and Fashion-MNIST), three fine-grained image datasets (CUB200-2011, Stanford-Cars, FGVC-Aircraft) and a COVID-19 dataset, and several backbone network models were used, including AlexNet, VGG16, ResNet18, ResNet101, InceptionV2 and EfficientNet-b0. Experimental results have shown that the recognition performance of the proposed method can significantly exceed the performance of existing state-of-the-art methods. The level of machine vision classification has reached the bottleneck, it is difficult to solve this problem by using a large-scale network model which has huge parameters that need to be optimized. We present an effective approach for breaking through the bottleneck of visual classification task by feature extraction with backbone DCNN and feature selective transformation with LSTM-RDN, separately. The code and pre-trained models are available from: https://github.com/lillllllll/LSTM-RDN

scholarly journals One-dimensional DCNN Feature Selective Transformation with LSTM-RDN for Image Classification

2021 ◽  
Author(s):  
li chaorong ◽  
Yuanyuan Huang ◽  
WEI HUANG ◽  
Fengqing Qin
Keyword(s):  
Feature Selection ◽  
Image Classification ◽  
Short Term Memory ◽  
Feature Learning ◽  
Network Models ◽  
Transformation Method ◽  
Deep Convolutional Neural Networks ◽  
One Dimensional ◽  
Selective Transformation ◽  
Image Datasets

Feature selection and transformation are the important techniques in machine learning field. A good feature selection / transformation method will greatly improve the performance of classification algorithm. In this work, we proposed a simple but efficient image classification method which is based on two-stage processing strategy. In the first processing stage, the one-dimensional features are obtained from image by transfer learning with the pre-trained Deep Convolutional Neural Networks (DCNN). These one-dimensional DCNN features still have the shortcomings of information redundancy and weak distinguishing ability. Therefore, it is necessary to use feature transformation to continue to obtain more distinguishable features. We propose a feature learning and selective transformation network based on Long Short-Term Memory (LSTM) combing ReLU and Dropout layers (called LSTM-RDN) to further process DCNN one-dimensional features. . The verification experiments were conducted on three public object image datasets(Cifar10, Cifar100 and Fashion-MNIST), three fine-grained image datasets(CUB200-2011, Stanford-Cars, FGVC-Aircraft) and a COVID-19 dataset. In the experiments, we used several backbone network models, including AlexNet, VGG16, ResNet18, ResNet101, InceptionV2 and EfficientNet-b0. Experimental results have shown that through feature selective transformation, the recognition accuracy of these DCNN models can significantly exceed the classification accuracies of the state-of-the-art methods.

scholarly journals One-dimensional DCNN Feature Selective Transformation with LSTM-RDN for Image Classification

2021 ◽  
Author(s):  
li chaorong ◽  
Yuanyuan Huang ◽  
WEI HUANG ◽  
Fengqing Qin
Keyword(s):  
Feature Selection ◽  
Image Classification ◽  
Short Term Memory ◽  
Feature Learning ◽  
Network Models ◽  
Transformation Method ◽  
Deep Convolutional Neural Networks ◽  
One Dimensional ◽  
Selective Transformation ◽  
Image Datasets

Feature selection and transformation are the important techniques in machine learning field. A good feature selection / transformation method will greatly improve the performance of classification algorithm. In this work, we proposed a simple but efficient image classification method which is based on two-stage processing strategy. In the first processing stage, the one-dimensional features are obtained from image by transfer learning with the pre-trained Deep Convolutional Neural Networks (DCNN). These one-dimensional DCNN features still have the shortcomings of information redundancy and weak distinguishing ability. Therefore, it is necessary to use feature transformation to continue to obtain more distinguishable features. We propose a feature learning and selective transformation network based on Long Short-Term Memory (LSTM) combing ReLU and Dropout layers (called LSTM-RDN) to further process DCNN one-dimensional features. . The verification experiments were conducted on three public object image datasets(Cifar10, Cifar100 and Fashion-MNIST), three fine-grained image datasets(CUB200-2011, Stanford-Cars, FGVC-Aircraft) and a COVID-19 dataset. In the experiments, we used several backbone network models, including AlexNet, VGG16, ResNet18, ResNet101, InceptionV2 and EfficientNet-b0. Experimental results have shown that through feature selective transformation, the recognition accuracy of these DCNN models can significantly exceed the classification accuracies of the state-of-the-art methods.

scholarly journals RSI-CB: A Large-Scale Remote Sensing Image Classification Benchmark Using Crowdsourced Data

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1594
Author(s):  
Haifeng Li ◽  
Xin Dou ◽  
Chao Tao ◽  
Zhixiang Wu ◽  
Jie Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Large Scale ◽  
Remote Sensing Image ◽  
National Standard ◽  
Natural Image ◽  
Deep Convolutional Neural Networks ◽  
Remote Sensing Image Classification ◽  
Crowdsourced Data ◽  
Classification Tasks

Image classification is a fundamental task in remote sensing image processing. In recent years, deep convolutional neural networks (DCNNs) have experienced significant breakthroughs in natural image recognition. The remote sensing field, however, is still lacking a large-scale benchmark similar to ImageNet. In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsourced data. Using crowdsourced data, such as Open Street Map (OSM) data, ground objects in remote sensing images can be annotated effectively using points of interest, vector data from OSM, or other crowdsourced data. These annotated images can, then, be used in remote sensing image classification tasks. Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification. This benchmark has large-scale geographical distribution and large total image number. It contains six categories with 35 sub-classes of more than 24,000 images of size 256 × 256 pixels. This classification system of ground objects is defined according to the national standard of land-use classification in China and is inspired by the hierarchy mechanism of ImageNet. Finally, we conduct numerous experiments to compare RSI-CB with the SAT-4, SAT-6, and UC-Merced data sets. The experiments show that RSI-CB is more suitable as a benchmark for remote sensing image classification tasks than other benchmarks in the big data era and has many potential applications.

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.

INFORMATION BLENDING IN VIRTUAL ASSOCIATIVE NETWORKS: A NEW PARADIGM FOR SENSOR INTEGRATION

1999 ◽  
Vol 08 (03) ◽  
pp. 275-290 ◽  
Author(s):  
YAN M. YUFIK ◽  
RAJ P. MALHOTRA
Keyword(s):  
Information Integration ◽  
Large Scale ◽  
Short Term Memory ◽  
Recognition Performance ◽  
Sensor Integration ◽  
Long Term Memory ◽  
Sensor Management ◽  
Dynamic Allocation ◽  
New Paradigm ◽  
Term Memory

Research reported in this article is motivated, in part, by current U.S. military programs aimed at the development of efficient data integration and sensor management methods capable of handling large sensor suites and achieving robust target recognition performance in real time scenarios. Modern sensor systems have shown good recognition abilities against a few isolated targets. However, these capabilities decline steeply when multiple sensors are acting against large target groups under realistic conditions requiring dynamic allocation of the sensor resources and efficient on-line integration and disambiguation of multiple sensor outputs. Neural networks and other sensor integration technologies have been inspired by cognitive models attributing human perceptual integration to parallel processing and convergence of simultaneous data streams. This article explores a different model emphasizing serial processing and association of consecutive memory traces in the Long Term Memory (LTM) into a globally connected memory structure called a Virtual Associative Network (VAN). Information integration in VAN is called blending. Target representation is constructed dynamically from the segments of virtual net matched serially against the input segments in the Short Term Memory (STM). This article will elaborate the concept of blending, reference its biological foundations, explain the difference between information blending and conventional sensor fusion techniques, and demonstrate blending applications in a large scale sensor management task.

scholarly journals Fast Spectral Clustering for Unsupervised Hyperspectral Image Classification

2019 ◽  
Vol 11 (4) ◽  
pp. 399 ◽  
Author(s):  
Yang Zhao ◽  
Yuan Yuan ◽  
Qi Wang
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Spectral Clustering ◽  
Large Scale ◽  
Environmental Science ◽  
Hyperspectral Image ◽  
Eigenvalue Decomposition ◽  
Hyperspectral Image Classification ◽  
Approximation Techniques ◽  
Image Datasets

Hyperspectral image classification is a challenging and significant domain in the field of remote sensing with numerous applications in agriculture, environmental science, mineralogy, and surveillance. In the past years, a growing number of advanced hyperspectral remote sensing image classification techniques based on manifold learning, sparse representation and deep learning have been proposed and reported a good performance in accuracy and efficiency on state-of-the-art public datasets. However, most existing methods still face challenges in dealing with large-scale hyperspectral image datasets due to their high computational complexity. In this work, we propose an improved spectral clustering method for large-scale hyperspectral image classification without any prior information. The proposed algorithm introduces two efficient approximation techniques based on Nyström extension and anchor-based graph to construct the affinity matrix. We also propose an effective solution to solve the eigenvalue decomposition problem by multiplicative update optimization. Experiments on both the synthetic datasets and the hyperspectral image datasets were conducted to demonstrate the efficiency and effectiveness of the proposed algorithm.

scholarly journals Current and Next Visit Prediction for Fatty Liver Disease with a Large-Scale Dataset (Preprint)

2020 ◽  
Author(s):  
ChengTse Wu ◽  
Ta-Wei Chu ◽  
Jyh-Shing Roger Jang
Keyword(s):  
Feature Selection ◽  
Liver Disease ◽  
Fatty Liver ◽  
Fatty Liver Disease ◽  
Large Scale ◽  
Prediction Models ◽  
Short Term Memory ◽  
Support Vector ◽  
Test Accuracy ◽  
Forward Selection

BACKGROUND Fatty liver disease (FLD) arises from the accumulation of fat in the liver and may cause liver inflammation which, according to past research it is shown that if not actively well-controlled, may develop into liver fibrosis, cirrhosis, or even hepatocellular carcinoma in the future. OBJECTIVE We describe the construction of machine-learning models for current-visit prediction (CVP) which can help physicians obtain more information for accurate diagnosis, and next-visit prediction (NVP) which can help physicians deal provide potential high-risk patients with advice to effectively prevent or delay health deterioration. METHODS The large-scale and high-dimensional dataset used in this study comes from the MJ Health Research Foundation in Taipei. The models we created use sequence forward selection (SFS) and one-pass ranking (OPR) for feature selection. For current-visit prediction (CVP), we explored multiple models including Adaboost, support vector machine (SVM), logistic regression (LR), random forest (RF), Gaussian Naïve Bayes (GNB), decision trees C4.5 (C4.5), and classification & regression trees (CART). For next-visit prediction (NVP), we used long short-term memory (LSTM) as a sequence classifier that uses various input sets for prediction. Model performance is evaluated based on two criteria: the accuracy of the test set, and the IoU and coverage between the features selected by OPR/SFS and by domain experts. RESULTS The dataset respectively includes 34,856 and 31,394 unique visits by male and female patients during 2009∼2016. The test accuracy results of CVP for Adaboost, SVM, LR, RF, GNB, C4.5, and CART were respectively 84.28, 83.84, 82.22, 82.21, 76.03, 75.78, and 75.53%. The test accuracy results of NVP of LSTM with fixed and variable intervals were respectively 78.20% and 76.79%. The proposed two paradigms of LSTM respectively achieved 39.29% and 41.21% error reduction when compared with a baseline model of simple induction. CONCLUSIONS This study explores a large fatty liver disease (FLD) dataset with high dimensionality. We have developed prediction models that can use for CVP and NVP for FLD prediction. We have also implemented efficient feature selection schemes for CVP and NVP to compare the automatically selected features with expert-selected features.

scholarly journals Parallel Connected Generative Adversarial Network with Quadratic Operation for SAR Image Generation and Application for Classification

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 871 ◽  
Author(s):  
Chu He ◽  
Dehui Xiong ◽  
Qingyi Zhang ◽  
Mingsheng Liao
Keyword(s):  
Image Classification ◽  
Large Scale ◽  
Statistical Characteristics ◽  
Sar Image ◽  
Deep Convolutional Neural Networks ◽  
Sar Images ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Optical Images ◽  
Sar Data

Thanks to the availability of large-scale data, deep Convolutional Neural Networks (CNNs) have witnessed success in various applications of computer vision. However, the performance of CNNs on Synthetic Aperture Radar (SAR) image classification is unsatisfactory due to the lack of well-labeled SAR data, as well as the differences in imaging mechanisms between SAR images and optical images. Therefore, this paper addresses the problem of SAR image classification by employing the Generative Adversarial Network (GAN) to produce more labeled SAR data. We propose special GANs for generating SAR images to be used in the training process. First, we incorporate the quadratic operation into the GAN, extending the convolution to make the discriminator better represent the SAR data; second, the statistical characteristics of SAR images are integrated into the GAN to make its value function more reasonable; finally, two types of parallel connected GANs are designed, one of which we call PWGAN, combining the Deep Convolutional GAN (DCGAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP) together in the structure, and the other, which we call CNN-PGAN, applying a pre-trained CNN as a discriminator to the parallel GAN. Both PWGAN and CNN-PGAN consist of a number of discriminators and generators according to the number of target categories. Experimental results on the TerraSAR-X single polarization dataset demonstrate the effectiveness of the proposed method.

A Large Size Image Classification Method Based on Semi-supervised Learning

2020 ◽  
Vol 13 (5) ◽  
pp. 669-680
Author(s):  
Dan Luo ◽  
Xili Wang
Keyword(s):  
Image Classification ◽  
Supervised Learning ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Main Idea ◽  
Label Propagation ◽  
Classification Method ◽  
Image Size ◽  
Large Size

Background: Semi-supervised learning in the machine learning community has received widespread attention. Semi-supervised learning can use a small number of tagged samples and a large number of untagged samples for efficient learning. Methods: In 2014, Kim proposed a new semi-supervised learning method: the minimax label propagation (MMLP) method. This method reduces time complexity to O (n), with a smaller computation cost and stronger classification ability than traditional methods. However, classification results are not accurate in large-scale image classifications. Thus, in this paper, we propose a semisupervised image classification method, which is an MMLP-based algorithm. The main idea is threefold: (1) Improving connectivity of image pixels by pixel sampling to reduce the image size, at the same time, reduce the diversity of image characteristics; (2) Using a recall feature to improve the MMLP algorithm; (3) through classification mapping, gaining the classification of the original data from the classification of the data reduction. Results: In the end, our algorithm also gains a minimax path from untagged samples to tagged samples. The experimental results proved that this algorithm is applicable to semi-supervised learning on small-size and that it can also gain better classification results for large-size image at the same time. Conclusion: In our paper, considering the connectivity of the neighboring matrix and the diversity of the characteristics, we used meanshift clustering algorithm, next we will use fuzzy energy clustering on our algorithm. We will study the function of these paths.

scholarly journals Large-scale comparative assessment of computational predictors for lysine post-translational modification sites

2018 ◽  
Vol 20 (6) ◽  
pp. 2267-2290 ◽  
Author(s):  
Zhen Chen ◽  
Xuhan Liu ◽  
Fuyi Li ◽  
Chen Li ◽  
Tatiana Marquez-Lago ◽  
...  
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Large Scale ◽  
Short Term Memory ◽  
Sequence Data ◽  
Model Performance ◽  
Structural Features ◽  
Computational Techniques ◽  
Post Translational Modification ◽  
Sequencing Data

Abstract Lysine post-translational modifications (PTMs) play a crucial role in regulating diverse functions and biological processes of proteins. However, because of the large volumes of sequencing data generated from genome-sequencing projects, systematic identification of different types of lysine PTM substrates and PTM sites in the entire proteome remains a major challenge. In recent years, a number of computational methods for lysine PTM identification have been developed. These methods show high diversity in their core algorithms, features extracted and feature selection techniques and evaluation strategies. There is therefore an urgent need to revisit these methods and summarize their methodologies, to improve and further develop computational techniques to identify and characterize lysine PTMs from the large amounts of sequence data. With this goal in mind, we first provide a comprehensive survey on a large collection of 49 state-of-the-art approaches for lysine PTM prediction. We cover a variety of important aspects that are crucial for the development of successful predictors, including operating algorithms, sequence and structural features, feature selection, model performance evaluation and software utility. We further provide our thoughts on potential strategies to improve the model performance. Second, in order to examine the feasibility of using deep learning for lysine PTM prediction, we propose a novel computational framework, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs), using deep, bidirectional, long short-term memory recurrent neural networks for accurate and systematic mapping of eight major types of lysine PTMs in the human and mouse proteomes. Extensive benchmarking tests show that MUscADEL outperforms current methods for lysine PTM characterization, demonstrating the potential and power of deep learning techniques in protein PTM prediction. The web server of MUscADEL, together with all the data sets assembled in this study, is freely available at http://muscadel.erc.monash.edu/. We anticipate this comprehensive review and the application of deep learning will provide practical guide and useful insights into PTM prediction and inspire future bioinformatics studies in the related fields.

Sign in / Sign up

Export Citation Format

Share Document