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Wavelet-based extended morphological profile and deep autoencoder for hyperspectral image classification

INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING 2018年第3期16卷

作者： Luo, Huiwu Tani, Yuan Yan Biuk-Aghai, Robert P. Yang, Xu Yang, Lina Wang, Yi Univ Macau Fac Sci & Technol Macau 999078 Peoples R China Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400030 Peoples R China

In this paper, we propose a novel scheme to learn high-level representative features and conduct classification for hyperspectral image (HSI) data in an automatic fashion. The proposed method is a collaboration of a wavelet-based extended morphological profile (WTEMP) and a deep autoencoder (DAE) ("WTEMP-DAE"), with the aim of exploiting the discriminative capability of DAE when using WTEMP features as the input. Each part of WTEMP-DAE is ingenious and contributes to the final classification performance. Specifically, in WTEMP-DAE, the spatial information is extracted from the WTEMP, which is then joined with the wavelet denoised spectral information to form the spectral-spatial description of HSI data. The obtained features are fed into DAE as the original input, where the good weights and bias of the network are initialized through unsupervised pre-training. Once the pro-training is completed, the reconstruction layers are discarded and a logistic regression (LR) layer is added to the top of the network to perform supervised fine-tuning and classification. Experimental results on two real HSI data sets demonstrate that the proposed strategy improves classification performance in comparison with other state-of-the-art hand-crafted feature extractors and their combinations.

关键词： Wavelet transform deep autoencoder neuronal network mathematical morphology feature extraction

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A novel deep autoencoder feature learning method for rotating machinery fault diagnosis

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2017年 95卷 187-204页

作者： Shao Haidong Jiang Hongkai Zhao Huiwei Wang Fuan Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China

The operation conditions of the rotating machinery are always complex and variable, which makes it difficult to automatically and effectively capture the useful fault features from the measured vibration signals, and it is a great challenge for rotating machinery fault diagnosis. In this paper, a novel deep autoencoder feature learning method is developed to diagnose rotating machinery fault. Firstly, the maximum correntropy is adopted to design the new deep autoencoder loss function for the enhancement of feature learning from the measured vibration signals. Secondly, artificial fish swarm algorithm is used to optimize the key parameters of the deep autoencoder to adapt to the signal features. The proposed method is applied to the fault diagnosis of gearbox and electrical locomotive roller bearing. The results confirm that the proposed method is more effective and robust than other methods. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： deep autoencoder Feature learning Fault diagnosis Maximum correntropy Artificial fish swarm algorithm

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Detection and classification of brain abnormality by a novel hybrid EfficientNet-deep autoencoder (EF-DA) CNN model from MRI brain images in smart health diagnosis

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INTERNATIONAL JOURNAL OF NANOTECHNOLOGY 2023年第5-10期20卷 696-718页

作者： Nayak, Dillip Ranjan Padhy, Neelamadhab Singh, Ashish Mallick, Pradeep Kumar GIET Univ Sch Engn & Technol CSE Gunupur 765022 Odisha India Deemed Univ Kalinga Inst Ind Technol Sch Comp Engn Bhubaneswar 751024 India

This paper presents the novel smart hybrid EfficientNet-deep autoencoder (EF-DA) deep Neural Network model to classify brain images. This is the succession of modified EfficientNetB0 with a deep autoencoder to detect tumours. Initially, the feature extraction is done by modified EfficientNet, and then classification is done by the proposed smart deep autoencoder. The images are filtered, cropped by morphological operations, and augmented to train a deep hybrid EF-DA model in the first stage. In the second stage, a modified deep autoencoder is used for classification. The statistical result analysis of the hybrid model is assessed using seven types of degree metrics like F-score, precision, recall, specificity, Kappa score, accuracy, and area under the ROC curve (AUC) score. It is compared with three types of pre-trained models like MobileNet, MobileNetV2, and ResNet50 for analysis. The EF-DA model has achieved an overall accuracy of 99.34% and an AUC score of 99.95%.

关键词： hybrid EfficientNet data augmentation deep autoencoder deep neural network AUC score overfitting recall precision F-score

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Classification of Motor Imagery EEG Signals Based on deep autoencoder and Convolutional Neural Network Approach

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IEEE ACCESS 2022年 10卷 48071-48081页

作者： Hwaidi, Jamal F. Chen, Thomas M. Univ London Dept Elect & Elect Engn London EC1V 0HB England

The technology of the brain-computer interface (BCI) employs electroencephalogram (EEG) signals to establish direct interaction between the human body and its surroundings with promising applications in medical rehabilitative services and cognitive science. deep learning approaches, particularly the detection and analysis of motor imagery signals using convolutional neural network (CNN) frameworks have produced outstanding results in the BCI system in recent years. The complex process of data representation, on the other hand, limits practical applications, and the end-to-end approach reduces the accuracy of recognition. Moreover, since noise and other signal sources can interfere with brain electrical capacitance, EEG classifiers are difficult to improve and have limited generalisation ability. To address these issues, this paper proposes a new approach for EEG motor imagery signal classification by using a variational autoencoder to remove noise from the signals, followed by a combination of deep autoencoder (DAE) and a CNN architecture to classify EEG motor imagery signals which is capable of training a deep neural network to replicate its input to output using encoding and decoding operations. Experimental results show that the proposed approach for motor imagery EEG signal classification is feasible and that it outperforms current CNN-based approaches and several traditional machine learning approaches.

关键词： Electroencephalography Feature extraction Convolutional neural networks deep learning Support vector machines Training Brain modeling Electroencephalography deep autoencoder convolutional neural network variational autoencoder motor imagery

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Image encryption based on logistic chaotic systems and deep autoencoder

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PATTERN RECOGNITION LETTERS 2022年 153卷 59-66页

作者： Sang, Yongpeng Sang, Jun Alam, Mohammad S. Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing Peoples R China Texas A&M Univ Kingsville Frank H Dotterweich Coll Engn Kingsville TX USA

In this paper, we propose a novel image encryption method based on logistic chaotic systems and deep autoencoder. In the encryption phase, first, the plaintext image is randomly scrambled by a logistic chaotic system. Then, the random scrambled image is encoded by a deep autoencoder to generate the ciphertext image. In order to obtain the ciphertext image with uniform distribution, we incorporated the uniform distribution constraint into the training of the deep autoencoder. The resulting ciphertext image contains high randomness, which is critical for an excellent image encryption algorithm. Histogram analysis, information entropy analysis, key space analysis, key sensitivity analysis, correlation analysis, and ablation experiments show that the proposed encryption algorithm can effectively resist attacks and has excellent encryption performance while providing high security. (c) 2021 Published by Elsevier B.V.

关键词： Image encryption Logistic chaotic systems deep autoencoder Uniform distribution

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TDEC: Evidential Clustering Based on Transfer Learning and deep autoencoder

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第10期32卷 5585-5597页

作者： Jiao, Lianmeng Wang, Feng Liu, Zhun-Ga Pan, Quan Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

Evidential clustering is a promising clustering framework using Dempster-Shafer belief function theory to model uncertain data. However, evidential clustering needs to estimate more parameters compared with other clustering algorithms, and thus the clustering performance of evidential clustering will be greatly affected if data is insufficient or contaminated. In addition, the existing evidential clustering algorithms can not well deal with high-dimensional data such as texts and images. To solve the above problems, an evidential clustering algorithm based on transfer learning and deep autoencoder (TDEC) is proposed. The TDEC utilizes deep autoencoder to obtain evidential clustering-friendly representations of the original data, and applies the maximum mean discrepancy (MMD) constraint between the source network and the target network, so that the network can learn domain-invariant features. The algorithm jointly trains the deep evidential clustering networks in the source domain and the target domain, and realizes the deep feature representations of high-dimensional data in the target domain for evidential clustering by minimizing reconstruction loss, entropy-based evidential clustering loss, MMD loss and the regular penalty term of the network parameters. In addition, an iterative optimization method to solve the TDEC objective function is proposed. Extensive experiments were conducted to evaluate the clustering performance of the proposed TDEC algorithm compared with the existing shallow transfer clustering algorithms and deep clustering algorithms. For both image and text clustering tasks, the proposed TDEC achieved approximately 5% performance improvement over the comparison algorithms on average. In addition, the practical application value of the proposed TDEC algorithm was demonstrated in unsupervised remote sensing image scene classification.

关键词： Clustering algorithms Partitioning algorithms Transfer learning Evidence theory Feature extraction Fuzzy systems Approximation algorithms deep autoencoder evidential clustering unsupervised transfer learning

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Community-aware dynamic network embedding by using deep autoencoder

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INFORMATION SCIENCES 2020年第0期519卷 22-42页

作者： Ma, Lijia Zhang, Yutao Li, Jianqiang Lin, Qiuzhen Bao, Qing Wang, Shanfeng Gong, Maoguo Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Hangzhou Elect Sci & Technol Univ Sch Comp Sci Hangzhou 310018 Peoples R China Xidian Univ Sch Elect Engn Key Lab Intelligent Percept & Image Understanding Minist Educ 2 South TaiBai Rd Xian 710071 Peoples R China

Network embedding has recently attracted lots of attention due to its wide applications on graph tasks such as link prediction, network reconstruction, node stabilization, and community stabilization, which aims to learn the low-dimensional representations of nodes with essential features. Most existing network embedding methods mainly focus on static or continuous evolution patterns of microscopic node and link structures in networks, while neglecting the dynamics of macroscopic community structures. In this paper, we propose a Community-aware Dynamic Network Embedding method (short for CDNE) which considers the dynamics of macroscopic community structures. First, we model the problem of dynamic network embedding as a minimization of an overall loss function, which tries to maximally preserve the global node structures, local link structures, and continuous community dynamics. Then, we adopt a stacked deep autoencoder algorithm to solve this minimization problem, obtaining the low-dimensional representations of nodes. Extensive experiments on both synthetic networks and real networks demonstrate the superiority of CDNE over the existing methods on tackling various graph tasks. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Network embedding Dynamic networks Community structures deep autoencoder Low-dimensional node representation

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Semantics-enhanced supervised deep autoencoder for depth image-based 3D model retrieval

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PATTERN RECOGNITION LETTERS 2019年 125卷 806-812页

作者： Siddiqua, Ayesha Fan, Guoliang Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

Increased accuracy and affordability of depth sensors such as Kinect has created a great depth-data source for various 3D oriented applications. Specifically, 3D model retrieval is attracting attention in the field of computer vision and pattern recognition due to its numerous applications. A cross-domain retrieval approach such as depth image based 3D model retrieval has the challenges of occlusion, noise and view variability present in both query and training data. In this paper, we propose a new supervised deep autoencoder approach followed by semantic modeling to retrieve 3D shapes based on depth images. The key novelty is the two-fold feature abstraction to cope with the incompleteness and ambiguity present in the depth images. First, we develop a supervised autoencoder to extract robust features from both real depth images and synthetic ones rendered from 3D models, which are intended to balance reconstruction and classification capabilities of mix-domain data. Then semantic modeling of the supervised autoencoder features offers the next level of abstraction to cope with the incompleteness and ambiguity of the depth data. It is interesting that unlike any other pairwise model structures, we argue that cross-domain retrieval is still possible using only one single deep network trained on real and synthetic data. The experimental results on the NYUD2 and ModelNet10 datasets demonstrate that the proposed supervised method outperforms the recent approaches for cross-modal 3D model retrieval. (C) 2019 Elsevier B.V. All rights reserved.

关键词： 3D model retrieval Shape matching Cross-modal retrieval deep autoencoder

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CDDA: color-dominant deep autoencoder for faster and efficient bilateral image filtering

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SIGNAL IMAGE AND VIDEO PROCESSING 2021年第6期15卷 1189-1195页

作者： Das, Apurba Shylaja, S. S. PES Univ Dept Comp Sci & Engn Bangalore Karnataka India

Nonlinear processing of high-dimensional data is quite common in image filtering algorithms. Bilateral, joint bilateral, and non-local means filters are the examples of the same. Real-time implementation of high-dimensional filters has always been a research challenge due to its computational complexity. In this paper, we have proposed a solution utilizing both color sparseness and color dominance in an image which ensures a faster algorithm for generic high-dimensional filtering. The solution speeds up the filtering algorithm further by psycho-visual saliency-based deep encoded dominant color gamut, learned for different subject classes of images. The proposed bilateral filter has been proved to be efficient both in terms of psycho-visual quality and performance for edge-preserving smoothing and denoising of color images. The results demonstrate competitiveness of our proposed solution with the existing fast bilateral algorithms in terms of the CTQ (critical to quality) parameters.

关键词： Range-domain filtering deep autoencoder weighted histogram fast algorithm color sparseness

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Joint network embedding of network structure and node attributes via deep autoencoder

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NEUROCOMPUTING 2022年 468卷 198-210页

作者： Pan, Yu Zou, Junhua Qiu, Junyang Wang, Shuaihui Hu, Guyu Pan, Zhisong Army Engn Univ Inst Command & Control Engn Nanjing Peoples R China Jiangnan Inst Comp Technol Math Engn & Adv Comp Wuxi Jiangsu Peoples R China

Network embedding aims to learn a low-dimensional vector for each node in networks, which is effective in a variety of applications such as network reconstruction and community detection. However, the majority of the existing network embedding methods merely exploit the network structure and ignore the rich node attributes, which tend to generate sub-optimal network representation. To learn more desired network representation, diverse information of networks should be exploited. In this paper, we develop a novel deep autoencoder framework to fuse topological structure and node attributes named FSADA. We firstly design a multi-layer autoencoder which consists of multiple non-linear functions to capture and preserve the highly non-linear network structure and node attribute information. Particularly, we adopt a pre-processing procedure to pre-process the original information, which can bet -ter facilitate to extract the intrinsic correlations between topological structure and node attributes. In addition, we design an enhancement module that combines topology and node attribute similarity to construct pairwise constraints on nodes, and then a graph regularization is introduced into the frame-work to enhance the representation in the latent space. Our extensive experimental evaluations demon-strate the superior performance of the proposed method. (c) 2021 Elsevier B.V. All rights reserved.

关键词： deep autoencoder deep learning Network analysis Network embedding Data mining Pattern recognition

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