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3rd IEEE International Conference on Image, Vision and Computing (ICIVC)

作者： Cai, Chengtao Wang, Boyu Liu, Yue Yan, Yongjie Harbin Engn Univ Coll Automat Harbin Heilongjiang Peoples R China State Key Lab Air Traff Management Syst & Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538649916

In machine vision, image processing technology is the basis of target recognition and positioning. When the background of the image is complex, especially when the background feature is similar to the target feature, the accuracy of the target recognition by traditional image processing methods cannot be guaranteed. In this paper, based on the background of automatic welding technology, proposing a new method of combining the neural networks and machine vision. Specifically, the image is preprocessed by using an improved convolutional auto-encoder to enhance the target features and remove the characteristics of the main interferers. Then, use the improved traditional image processing technology to extract the target and complete the processing of the featureless image. Finally, use a binocular camera to achieve accurate positioning of the target. This paper provides a new idea for the identification and positioning of the target.

关键词： neural networks machine vision weld positioning featureless image convolutional auto-encoder

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UNSUPERVISED DEEP TRANSFER FEATURE LEARNING FOR MEDICAL IMAGE CLASSIFICATION 16

UNSUPERVISED DEEP TRANSFER FEATURE LEARNING FOR MEDICAL IMAG...

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16th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Ahn, Euijoon Kumar, Ashnil Feng, Dagan Fulham, Michael Kim, Jinman Univ Sydney Sch Comp Sci Sydney NSW Australia Royal Prince Alfred Hosp Dept Mol Imaging Camperdown NSW Australia Univ Sydney Sydney Med Sch Sydney NSW Australia Shanghai Jiao Tong Univ Med X Res Inst Shanghai Peoples R China

ISBN: (纸本)9781538636411

The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale, annotated training data. However, there is a paucity of annotated data available due to the complexity of manual annotation. To overcome this problem, a popular approach is to use transferable knowledge across different domains by: 1) using a generic feature extractor that has been pre-trained on large-scale general images (i.e., transfer learned) but which not suited to capture characteristics from medical images;or 2) fine-tuning generic knowledge with a relatively smaller number of annotated images. Our aim is to reduce the reliance on annotated training data by using a new hierarchical unsupervised feature extractor with a convolutional auto-encoder placed atop of a pre-trained convolutional neural network. Our approach constrains the rich and generic image features from the pre-trained domain to a sophisticated representation of the local image characteristics from the unannotated medical image domain. Our approach has a higher classification accuracy than transfer-learned approaches and is competitive with state-ofthe-art supervised fine-tuned methods.

关键词： Deep Learning convolutional Neural Network Unsupervised Feature Learning convolutional auto-encoder Modality Classification

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Deep Supervised auto-encoder Hashing for Image Retrieval 1st

Deep Supervised Auto-encoder Hashing for Image Retrieval

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1st Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Tang, Sanli Chi, Haoyuan Yang, Jie Huang, Xiaolin Zareapoor, Masoumeh Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai Peoples R China

ISBN: (纸本)9783030033354;9783030033347

Image hashing approaches map high dimensional images to compact binary codes that preserve similarities among images. Although the image label is important information for supervised image hashing methods to generate hashing codes, the retrieval performance will be limited according to the performance of the classifier. Therefore, an effective supervised auto-encoder hashing method (SAEH) is proposed to generate low dimensional binary codes in a point-wise manner through deep convolutional neural network. The auto-encoder structure in SAEH is designed to simultaneously learn image features and generate hashing codes. Moreover, some extra relaxations for generating binary hash codes are added to the objective function. The extensive experiments on several large scale image datasets validate that the auto-encoder structure can indeed increase the performance for supervised hashing and SAEH can achieve the best image retrieval results among other prominent supervised hashing methods.

关键词： Image retrieval Image hashing Supervised learning Deep neural network convolutional auto-encoder

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Simplifying Reinforced Feature Selection via Restructured Choice Strategy of Single Agent 20

Simplifying Reinforced Feature Selection via Restructured Ch...

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20th IEEE International Conference on Data Mining (ICDM)

作者： Zhao, Xiaosa Liu, Kunpeng Fan, Wei Jiang, Lu Zhao, Xiaowei Yin, Minghao Fu, Yanjie Northeast Normal Univ Changchun Peoples R China Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA

ISBN: (纸本)9781728183169

Feature selection aims to select a subset of features to optimize the performances of downstream predictive tasks. Recently, multi-agent reinforced feature selection (MARFS) has been introduced to automate feature selection, by creating agents for each feature to select or deselect corresponding features. Although MARFS enjoys the automation of the selection process, MARFS suffers from not just the data complexity in terms of contents and dimensionality, but also the exponentially-increasing computational costs with regard to the number of agents. The raised concern leads to a new research question: Can we simplify the selection process of agents under reinforcement learning context so as to improve the efficiency and costs of feature selection? To address the question, we develop a single-agent reinforced feature selection approach integrated with restructured choice strategy. Specifically, the restructured choice strategy includes: 1) we exploit only one single agent to handle the selection task of multiple features, instead of using multiple agents. 2) we develop a scanning method to empower the single agent to make multiple selection/deselection decisions in each round of scanning. 3) we exploit the relevance to predictive labels of features to prioritize the scanning orders of the agent for multiple features. 4) we propose a convolutional auto-encoder algorithm, integrated with the encoded index information of features, to improve state representation. 5) we design a reward scheme that take into account both prediction accuracy and feature redundancy to facilitate the exploration process. Finally, we present extensive experimental results to demonstrate the efficiency and effectiveness of the proposed method.

关键词： feature selection reinforcement learning convolutional auto-encoder

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An Adaptive Convolution auto-encoder Based on Spiking Neurons 29th

An Adaptive Convolution Auto-encoder Based on Spiking Neuron...

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29th International Conference on Neural Information Processing

作者： Zhu, Chuanmeng Jiang, Jiaqiang Jiang, Runhao Yan, Rui Zhejiang Univ Technol Coll Comp Sci Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9783031301070;9783031301087

Neural coding is one of the central questions in neuroscience for converting visual information into spike patterns. However, the existing encoding techniques require a preset time window and lack effective learning. In order to overcome these two problems, we design an adaptive convolutional auto-encoder based on spiking neurons in this paper. We first exploit the spike pixel mapping decoding approach to find the optimal value of the time window automatically. Next, we design a deep convolutional neural network to adapt the learning parameters by reconstruction errors to realize the spike encoding process. Then we can naturally get coding pre-training parameters for unifying the convolutional spike coding layer with back-end deep spiking neural networks (SNNs) for recognition tasks. Simulation results demonstrate that the proposed method can achieve better performance compared with other encoding methods.

关键词： Neural coding convolutional auto-encoder Encoding time window Spike decoding Reconstruction error SNNs

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Reinforcement Learning Fault Diagnosis Method Based on Less Tag Data 6th

Reinforcement Learning Fault Diagnosis Method Based on Less ...

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Conference of The-Efficiency-and-Performance-Engineering-Network (TEPEN) / 6th International conference on Maintenance Engineering (IncoME)

作者： Xin, Kuo Wang, Jianguo Zhang, Wenxing Inner Mongolia Univ Sci & Technol Coll Mech Engn Baotou 014010 Inner Mongolia Peoples R China

ISBN: (纸本)9783030990756;9783030990749

Vibration signals are often used in the fault diagnosis of rotating machinery. However, due to the influence of complex environment, environmental noise is often doped, and the diagnostic accuracy is reduced. The traditional deep self-encoder is used in the noise reduction process of rotating machinery fault diagnosis. The pooling model is poor and easy to lead to over-fitting problems, and deep learning training needs a large number of labeled data. Therefore, this paper proposes a reinforcement learning fault diagnosis method based on less label data. The random pooling is used to replace the pooling layer of the original convolutional self-encoder, and the exponential linear unit (ELU) is used to replace the original activation function to enhance the convolutional self-encoder. A large number of unlabeled samples are used for training, and then the deep reinforcement learning is used for network fine tuning. The experimental results of the sensor data collected by the fault diagnosis test bench show that the method used has a good improvement in denoising ability and feature extraction ability, and the recognition accuracy and stability are better than traditional convolutional autoencoder and traditional machine learning methods.

关键词： Reinforcement learning Stochastic pooling convolutional auto-encoder Fault diagnosis

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Deep robust multi-channel learning subspace clustering networks

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IMAGE AND VISION COMPUTING 2023年第1期137卷

作者： Fang, Mengzhu Gao, Wei Feng, Zirui Northeast Normal Univ Sch Math & Stat Changchun 130024 Jilin Peoples R China Loughborough Univ Sch Business & Econ Loughborough LE11 3TU England

Subspace clustering methods are now widely used for unsupervised high-dimensional data processing in computer vision and other domains. Deep subspace clustering methods based on auto-encoder networks have made a significant improvement in nonlinear subspace clustering problems in comparison to previous works. However, these methods ignore the valid information lost during feature extraction, resulting in incomplete information and imprecise feature representations for subspace clustering. In addition, the clustering performance of the existing clustering methods is excessively dependent on hyper-parameters, making training difficult and unstable. In this paper, we propose Deep Robust Multi-Channel Learning Subspace Clustering Networks (DRMCLSC), a novel deep subspace clustering network for learning more comprehensive feature representations with good robustness for subspace clustering. The multi-channel learning strategy allows the model to extract, retain and fuse features simultaneously, enabling all valid information from the sample data to be obtained. Moreover, the multi-channel learning structure of the proposed method produces a more stable integration network that is less dependent on hyper-parameters and more resistant to training errors than previous works. Extensive experimental results on four benchmark datasets demonstrate the proposed method is superior and more effective than the state-of-the-art subspace clustering methods.

关键词： Subspace clustering Deep networks Unsupervised learning Multi-channel learning convolutional auto-encoder Feature extraction

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Classification of COVID-19 Patient Based on Multilayer Perceptron Neural Networks Optimized with Garra Rufa Fish Optimization Using CT Scan Images

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2023年第6期37卷 2357003-2357003页

作者： Selvaraj, Vairaprakash Alagarsamy, Manjunathan Thangaraju, Dineshkumar Mani, Dinesh Paramathi Ramco Inst Technol Dept Elect & Commun Engn Rajapalayam 626117 Tamil Nadu India K Ramakrishnan Coll Technol Dept Elect & Commun Engn Trichy 621112 Tamil Nadu India Kongunadu Coll Engn & Technol Dept Elect & Commun Engn Trichy 621215 Tamil Nadu India Sona Coll Technol Dept Elect & Commun Engn Salem 636005 Tamil Nadu India

COVID-19 is known in recent times as a severe syndrome of respiratory organ (Lungs) and has gradually produced pneumonia, a lung disorder all around the world. As coronavirus is continually spreading rapidly globally, the computed tomography (CT) technique has been made important and essential for quick diagnosis of this dangerous syndrome. Hence, it is necessitated to develop a precise computer-based technique for assisting medical clinicians in identifying the COVID-19 influenced patients with the help of CT scan images. Therefore, the multilayer perceptron neural networks optimized with Garra Rufa Fish optimization using images of CT scan is proposed in this paper for the classification of COVID-19 patients (COV-19-MPNN-GRF-CTI). The input images are taken from SARS-COV-2 CT-scan dataset. Initially, the input images are pre-processed utilizing convolutional auto-encoder (CAE) to enhance the quality of the input images by eliminating noises. The pre-processed images are fed to Residual Network (ResNet-50) for extracting the global and statistical features. The extraction over the features of CT scan images is made through ResNet-50 and subsequently input to multilayer perceptron neural networks (MPNN) for CT images classification as COVID-19 and Non-COVID-19 patients. Here, the layer of Batch Normalization of the MPNN is separated and added with ResNet-50 layer. Generally, MPNN classifier does not divulge any adoption of optimization approach for calculating the optimal parameters and accurately classifying the extracted features of CT images. The Garra Rufa Fish (GRF) optimization algorithm performs to optimize the weight parameters of MPNN classifiers. The proposed approach is executed in MATLAB. The performance metrics, such as sensitivity, precision, specificity, F-measure, accuracy and error rate, are examined. Then the performance of the proposed COV-19-MPNN-GRF-CTI method provides 22.08%, 24.03%, 34.76% higher accuracy, 23.34%, 26.45%, 34.44% higher precisi

关键词： Coronavirus SARS-COV-2 CT-scan dataset convolutional auto-encoder Residual Network (ResNet-50) multilayer perceptron neural networks Garra Rufa Fish (GRF) optimization algorithm

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auto-encoder Learning-Based UAV Communications for Livestock Management

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DRONES 2022年第10期6卷 276-276页

作者： Alanezi, Mohammed A. Mohammad, Abdullahi Sha'aban, Yusuf A. Bouchekara, Houssem R. E. H. Shahriar, Mohammad S. Univ Hafr Al Batin Dept Comp Sci & Engn Technol Hafar al Batin 31991 Saudi Arabia Ahmadu Bello Univ Dept Comp Engn Zaria 810001 Nigeria UCL Dept Elect & Elect Engn London WC1E 7JE England Univ Hafr Al Batin Dept Elect Engn Hafar al Batin 31991 Saudi Arabia

The advancement in computing and telecommunication has broadened the applications of drones beyond military surveillance to other fields, such as agriculture. Livestock farming using unmanned aerial vehicle (UAV) systems requires surveillance and monitoring of animals on relatively large farmland. A reliable communication system between UAVs and the ground control station (GCS) is necessary to achieve this. This paper describes learning-based communication strategies and techniques that enable interaction and data exchange between UAVs and a GCS. We propose a deep auto-encoder UAV design framework for end-to-end communications. Simulation results show that the auto-encoder learns joint transmitter (UAV) and receiver (GCS) mapping functions for various communication strategies, such as QPSK, 8PSK, 16PSK and 16QAM, without prior knowledge.

关键词： unmanned aerial vehicle convolutional auto-encoder livestock farming deep neural networks

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Attention-Aware Spectral Difference Representation for Hyperspectral Anomaly Detection

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REMOTE SENSING 2023年第10期15卷 2652-2652页

作者： Zhang, Wuxia Guo, Huibo Liu, Shuo Wu, Siyuan Xian Univ Posts & Telecommun Sch Comp Sci & Technol Shaanxi Key Lab Network Data Anal & Intelligent Pr Xian 710121 Peoples R China Chengdu Univ Informat Technol Dept Elect Engn Chengdu 610103 Peoples R China Xian Univ Technol Coll Comp Sci & Engn Xian 710048 Peoples R China

Hyperspectral Anomaly Detection (HAD) aims to detect the pixel or target whose spectral characteristics are significantly different from the surrounding pixels or targets. The effectiveness of reconstructing the background model is an essential element affecting the improvement of the HAD performance. This paper proposes a Hyperspectral Anomaly Detection method based on Attention-aware Spectral Difference Representation (HAD-ASDR) to reconstruct more accurate background models by using the generated noise distribution matchable to the background as input. The proposed HAD-ASDR mainly includes three modules: Attention-aware Spectral Difference Representation Module (ASDRM), convolutional auto-encoder based Background Reconstruction Module (CAE-BRM) and Joint Spectrum Intensity and Angle based Anomaly Detection Module (JSIA-ADM). First, inspired by Generative Adversarial Network (GAN), ASDRM is proposed to generate a noise distribution that better matches the background by the attention mechanism and the different operation. Then, CAE-BRM is employed to reconstruct the accurate background using the generated noise distribution as input and the convolutional auto-encoder with skip connections. Finally, JSIA-ADM is presented to detect anomalies more accurately by calculating the reconstructed errors from both spectral intensity and angle perspectives. The proposed HAD-ASDR has been verified on five data sets and achieves better or comparable HAD results compared to six other comparison methods. The average AUC of HAD-ASDR on these five data sets is 0.9817 higher than that of the comparison methods, resulting in an improvement of 0.0253. The experimental results demonstrate its superior performance and stability.

关键词： anomaly detection hyperspectral image attention mechanism generative adversarial network convolutional auto-encoder

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