A CNN-based face detector with a simple feature map and a coarse-to-fine classifier - Withdrawn

Chen, Ying Nong; Han, Chin Chuan; Wang, Cheng Tzu; Jeng, Bor-Shenn; Fan, Kuo-Chin

doi:10.1109/tpami.2007.70798

Cited by 12 publications

(5 citation statements)

References 26 publications

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“…Each monitoring station collects air quality data once an hour, and the dataset contains more than 400000 instances, each with concentrations of CO and N O. To prove the effectiveness of the proposed Attention-CNN-LSTM, several models are used for comparison, including ARIMA [13], SVR [14], CNN [15], LSTM [16], and CNN-LSTM [17]. In order to prevent inconsistency of data magnitude differences and gradient explosion, we need to convert all the input data by scaling the attributes between [0, 1] using the min-max normalization.…”

Section: Performance Metric and Settingsmentioning

confidence: 99%

Predicting of air pollutant concentrations based on spatio-temporal attention convolutional LSTM networks

Jiang¹,

Bychkov²,

Li³

et al. 2021

1st International Workshop on Advanced Information and Computation Technologies and Systems 2020

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Forecasting of air pollutant concentration, which is influenced by air pollution accumulation, traffic flow and industrial emissions, has attracted extensive attention for decades. In this paper, we propose a spatio-temporal attention convolutional long short term memory neural networks (Attention-CNN-LSTM) for air pollutant concentration forecasting. Firstly, we analyze the Granger causalities between different stations and establish a hyperparametric Gaussian vector weight function to determine spatial autocorrelation variables, which is used as part of the input feature. Secondly, convolutional neural networks (CNN) is employed to extract the temporal dependence and spatial correlation of the input, while feature maps and channels are weighted by attention mechanism, so as to improve the effectiveness of the features. Finally, a depth long short term memory (LSTM) based time series predictor is established for learning the long-term and short-term dependence of pollutant concentration. In order to reduce the effect of diverse complex factors on LSTM, inherent features are extracted from historical air pollutant concentration data meteorological data and timestamp information are incorporated into the proposed model. Extensive experiments were performed using the Attention-CNNLSTM, autoregressive integrated moving average (ARIMA), support vector regression (SVR), traditional LSTM and CNN, respectively. The results demonstrated that the feasibility and practicability of Attention-CNN-LSTM on estimating CO and NO concentration.

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Section: Performance Metric and Settingsmentioning

confidence: 99%

Predicting of air pollutant concentrations based on spatio-temporal attention convolutional LSTM networks

Jiang¹,

Bychkov²,

Li³

et al. 2021

1st International Workshop on Advanced Information and Computation Technologies and Systems 2020

View full text Add to dashboard Cite

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“…The image training dataset of each classifier was divided into batches [50] to enable faster training and minimize the memory requirement for classification. Decreasing the batch size will reduce the classifier prediction accuracy, whereas increasing the batch size will increase the needed memory for classification and also cause model overfitting that might not provide sufficient help for identifying image features [51]. Therefore, a compromise on batch size, small to adequately large (10-70), is considered in our classifier architecture to achieve the highest model accuracy in the minimum classification time, and hence enhances the cloud system performance.…”

Section: Wcnn Image Classifiers Datasetsmentioning

confidence: 99%

Utilizing Convolutional Neural Networks for Image Classification and Securing Mobility of People With Physical and Mental Disabilities in Cloud Systems

et al. 2020

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Image recognition is widely used for detecting human obstructions and identifying people with disabilities. The accuracy of identifying images of handicapped people is powered by image classification techniques that are based on deep learning methodologies. Specifically, convolutional neural networks are employed to improve image classification of people with mental and physical disabilities. In this research, images of people with different disabilities are used to extract hidden features that symbolize each disability. Three different deep learning image classifiers are built to classify images of people in wheelchairs, blind people, and people with Down syndrome. A security technique is developed that is based on multiprotocol label switching headers to secure the image mobility over cloud nodes. The proposed approach is validated by measuring the impact of the deep learning image classifiers on image classification and securing image mobility on cloud system performance. The experimental results show the effectiveness of the proposed approach in improving image prediction of disabled people and enhancing the performance of securing image mobility in cloud systems.INDEX TERMS deep learning, image classification, wide convolutional neural networks, image feature maps, securing image mobility, MPLS header

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“…In recent years, because machine learning does not need to change the topological structure of the images, it is very popular in image recognition. Convolution neural network (CNN) is not only one of the deep learning [28] but also one of the artificial neural networks, which mainly is used in the fields of speech analysis [29] and image recognition [30].…”

Section: Cnn and Elmmentioning

confidence: 99%

A Method Combining CNN and ELM for Feature Extraction and Classification of SAR Image

Wang

Zhang

2019

Journal of Sensors

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Due to the large number of Sigmoid activation function derivation in the traditional convolution neural network (CNN), it is difficult to solve the question of the low efficiency of extracting the feature of Synthetic Aperture Radar (SAR) images. The Sigmoid activation function in the CNN is improved to be a rectified linear unit (ReLU) activation function, and the classifier is modified by the Extreme Learning Machine (ELM). Finally, in this CNN model, the improved CNN works as the feature extractor and ELM performs as a recognizer. A SAR image recognition algorithm based on the CNN-ELM algorithm is proposed by combining the CNN and the ELM algorithm. The experiment is conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database which contains 10 kinds of target images. The experiment result shows that the algorithm can realize the sparsity of the network, alleviate the overfitting problem, and speed up the convergence speed of the network. It is worth mentioning that the running time of this experiment is very short. Compared with other experiment on the same database, it indicates that this experiment has generated a higher recognition rate. The accuracy of the SAR image recognition is 100%.

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A CNN-based face detector with a simple feature map and a coarse-to-fine classifier - Withdrawn

Cited by 12 publications

References 26 publications

Predicting of air pollutant concentrations based on spatio-temporal attention convolutional LSTM networks

Predicting of air pollutant concentrations based on spatio-temporal attention convolutional LSTM networks

Utilizing Convolutional Neural Networks for Image Classification and Securing Mobility of People With Physical and Mental Disabilities in Cloud Systems

A Method Combining CNN and ELM for Feature Extraction and Classification of SAR Image

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