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Deep neural network for halftone image classification based on sparse auto-encoder

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2016年 50卷 245-255页

作者： Zhang, Yan Zhang, Erhu Chen, Wanjun Xian Univ Technol Fac Mech & Precis Instrumental Engn Xian 710048 Peoples R China Armed Police Acad Dept Border Control & Immigrat Langfang 065000 Peoples R China Xian Univ Technol Fac Printing Packing Engn & Digital Media Technol Xian 710048 Peoples R China

To restore high quality continuous tone images from each class of halftone images, halftone image fine classification is the key problem. In this paper, a novel feature learning method is proposed for classifying 14 kinds of halftone images produced by the most well-known halftoning algorithms. This study employs the stacked sparse auto-encoders (SAE) trained with unsupervised learning for extracting features of halftone images, and then uses softmax regression with supervised learning for fine-tuning the deep neural network and classifying halftone images. In order to reduce the run-time of deep neural network and improve the image correct classification rate, we propose an effective patch extraction method for testing halftone images by measuring the mean and variance of local entropy in a patch. Halftone image classification is determined by the classification results of all effective patches inside an image via majority voting (MV). The experimental results demonstrate that our proposed method achieves an average Correct classification rate (ACCR) of over 99.44% for 14 kinds of halftone images on two public image sets. Compared with state-of-the-art LMS-Bayes and M-10-ML methods, the proposed SAE-MV method can distinguish the most categories of halftone images and achieve competitive ACCR, meanwhile, demonstrate better generalization performance. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Halftone image classification sparse auto-encoder Effective patch extraction Majority voting

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A sparse auto-encoder-based deep neural network approach for induction motor faults classification

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MEASUREMENT 2016年 89卷 171-178页

作者： Sun, Wenjun Shao, Siyu Zhao, Rui Yan, Ruqiang Zhang, Xingwu Chen, Xuefeng Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Jiangsu Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Nanyang Ave Singapore 639798 Singapore Xi An Jiao Tong Univ Collaborat Innovat Ctr High End Mfg Equipment Xian 710049 Peoples R China

This paper presents a deep neural network (DNN) approach for induction motor fault diagnosis. The approach utilizes sparse auto-encoder (SAE) to learn features, which belongs to unsupervised feature learning that only requires unlabeled measurement data. With the help of the denoising coding, partial corruption is added into the input of the SAE to improve robustness of feature representation. Features learned from the SAE are then used to train a neural network classifier for identifying induction motor faults. In addition, to prevent overfitting during the training process, a recently developed regularization method called "dropout" which has been proved to be very effective in neural network was employed. An experiment performed on a machine fault simulator indicates that compared with traditional neural network, the SAE-based DNN can achieve superior performance for feature learning and classification in the field of induction motor fault diagnosis. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： sparse auto-encoder Deep neural network Fault diagnosis Denoising Dropout

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Layerwise feature selection in Stacked sparse auto-encoder for tumor type prediction

Layerwise feature selection in Stacked Sparse Auto-Encoder f...

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IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Singh, Vikas Baranwal, Nikhil Sevakula, Rahul K. Verma, Nishchal K. Cui, Yan Indian Inst Technol Kanpur Dept Elect Engn Kanpur Uttar Pradesh India Indian Inst Technol Kanpur Fac Elect Engn Kanpur Uttar Pradesh India Univ Tennessee Hlth Sci Ctr Fac Microbiol Immunol & Biochem Knoxville TN 37996 USA

ISBN: (纸本)9781509016105

Transcriptome data has been proved to be very valuable for clinical applications, such as diagnosis and prognosis of various cancers. In this paper, we present layer-wise feature selection in conjunction with stacked sparse autoencoders (SSAE), a deep learning strategy for tumor classification with gene expression data. While SSAE learns high-level features from data, performing feature selection in every layer is a heuristic to obtain relevant features at every stage and also to assist in reducing the computation during fine-tuning procedure. The data in the new feature representation is finally used by classifier(s) to perform Tumor detection. The algorithm was tested on 36 datasets from the GEMLeR repository and w.r.t. AUC (Area under ROC curve) performance, it was found to outperform the GEMLeR benchmark results on 35 datasets (tied on the other dataset).

关键词： sparse auto-encoder cancer classification deep learning feature selection

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Deep Representations Based on sparse auto-encoder Networks for Face Spoofing Detection 11th

Deep Representations Based on Sparse Auto-Encoder Networks f...

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11th Chinese Conference on Biometric Recognition (CCBR)

作者： Yang, Dakun Lai, Jianhuang Mei, Ling Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9783319466545;9783319466538

automatic face recognition plays significant role in biometrics systems, and face spoofing has raised concerns at the same time, since a photo or video of an authorized uesr's face could be used for deceiving the system. In this paper, we propose a new hierarchical visual feature based on deep learning to discriminate spoof face. First, the LBP descriptor is used to extract low level face features of face images, and then these low level features are encoded to high level features via a deep learning architecture which is consists of sparse auto-encoder (SAE). Finally, SVM classifier is applied to detect face spoofing. We perform a experimental evaluation on two face liveness detection databases, CASIA database and NUAA database. The results indicate the robustness of the proposed approach for face spoofing detection.

关键词： LBP Deep learning sparse auto-encoder SVM Face spoofing

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sparse auto-encoder based feature learning for human body detection in depth image

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SIGNAL PROCESSING 2015年 112卷 43-52页

作者： Su, Song-Zhi Liu, Zhi-Hui Xu, Su-Ping Li, Shao-Zi Ji, Rongrong Xiamen Univ Sch Informat Sci & Technol Xiamen 361005 Peoples R China Xiamen Univ Fujian Key Lab Brain Like Intelligent Syst Xiamen 361005 Peoples R China

Human body detection in depth image is an active research topic in computer vision. But depth feature extraction is still an open problem. In this paper, a novel feature learning method based on sparse auto-encoder (SAE) is proposed for human body detection in depth image. The proposed learning based feature enables capturing the intrinsic human body structure. To further reduce the computation cost of SAE, both convolution neural network and pooling are introduced to reduce the training complexity. In addition, upon learning SAE based depth feature, we further pursuit the detector efficiency. A beyond sliding window localization strategy is proposed based on the fact that the depth values of object surface are almost the same. The proposed localization strategy first uses the histogram of depth to generate candidate detection window center, and then exploits the relationship between human body height and depth to determine the detection window size. Thus, it can avoid the time-consuming sliding window search, and further enables fast human body localization. Experiments on SZU Depth Pedestrian dataset verify the effectiveness of our proposed method. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Human detection Depth image Feature learning sparse auto-encoder

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Locality-Constrained sparse auto-encoder for Image Classification

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IEEE SIGNAL PROCESSING LETTERS 2015年第8期22卷 1070-1073页

作者： Luo, Wei Yang, Jian Xu, Wei Fu, Tao Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China

We propose a locality-constrained sparse auto-encoder (LSAE) for image classification in this letter. Previous work has shown that the locality is more essential than sparsity for classification task. We here introduce the concept of locality into the auto-encoder, which enables the auto-encoder to encode similar inputs using similar features. The proposed LSAE can be trained by the existing backprop algorithm;no complicated optimization is involved. Experiments on the CIFAR-10, STL-10 and Caltech-101 datasets validate the effectiveness of LSAE for classification task.

关键词： Feature learning image classification sparse auto-encoder

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A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression

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BIO-MEDICAL MATERIALS AND ENGINEERING 2015年第Sup1期26卷 S1549-S1558页

作者： Yang, Jianli Bai, Yang Li, Guojun Liu, Ming Liu, Xiuling Hebei Univ Coll Elect & Informat Engn Key Lab Digital Med Engn Hebei Prov Baoding 071000 Hebei Peoples R China

Premature ventricular contraction (PVC) is one of the most serious arrhythmias. Without early diagnosis and proper treatment, PVC can result in significant complications. In this paper, a novel feature extraction method based on a sparse auto-encoder (SAE) and softmax regression (SR) classifier was used to differentiate PVCs from other common Non-PVC rhythms, including normal sinus (N), left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contraction (APC), and paced beat (PB) rhythms. The proposed method was analyzed using 40 ECG records obtained from the MIT-BIH Arrhythmia Database. The proposed method exhibited an overall accuracy of 99.4%, with a PVC recognition sensitivity and positive predictability of 97.9% and 91.8%, respectively.

关键词： PVC diagnosis sparse auto-encoder softmax regression feature extraction

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Large data density peak clustering based on sparse autoencoder and data space meshing via evidence probability distribution

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2025年第1期12卷 1-15页

作者： Lu, Fang Harbin Finance Univ Harbin 150000 Peoples R China

The development of big data analysis technology has brought new development opportunities to the production and management of various industries. Through the mining and analysis of various data in the operation process of enterprises by big data technology, the internal associated data of the enterprises and even the entire industry can be obtained. As a common method for large-scale data statistical analysis, clustering technology can effectively mine the relationship within massive heterogeneous multidimensional data, complete unlabeled data classification, and provide data support for various model analysis of big data. Common big data density clustering methods are time-consuming and easy to cause errors in data density allocation, which affects the accuracy of data clustering. Therefore we propose a novel large data density peak clustering based on sparse auto-encoder and data space meshing via evidence probability distribution. Firstly, the sparse auto-encoder in deep learning is used to achieve feature extraction and dimensionality reduction for input high-dimensional data matrix through training. Secondly, the data space is meshed to reduce the calculation of the distance between the sample data points. When calculating the local density, not only the density value of the grid itself, but also the density value of the k nearest neighbors are considered, which reduces the influence of the subjective selection truncation distance on the clustering results and improves the clustering accuracy. The grid density threshold is set to ensure the stability of the clustering results. Using the K-nearest neighbor information of the sample points, the transfer probability distribution strategy and evidence probability distribution strategy are proposed to optimize the distribution of the remaining sample points, so as to avoid the joint error of distribution. The experimental results show that the proposed algorithm has higher clustering accuracy and better clustering p

关键词： data density clustering sparse auto-encoder data space meshing evidence probability distribution transfer probability distribution strategy

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License Plate Detection Based On sparse auto-encoder 8

License Plate Detection Based On Sparse Auto-Encoder

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8th International Symposium on Computational Intelligence and Design (ISCID)

作者： Yang, Ran Yin, Huarui Chen, Xiaohui Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei Peoples R China

ISBN: (纸本)9781467395878

In modern society, automatic license plate recognition (ALPR) plays an important role in the field of Intelligent Transport Systems (ITS). In order to recognize the license plate efficiently, the location of the license plate must be detected first. In consequence, the detection of the license plate becomes a crucial stage in an ALPR system, affecting the performance of the whole system enormously. In this paper, we propose a novel method based on sparse auto-encoder (SAE) to detect the vehicle license plate. The proposed method consists of three main stages: (1) A block-based image segmentation technique used for dividing the image into several blocks. (2) Deep learning model (SAE) trained for candidate block selection. (3) Accurate extraction of the license plate. Unlike other existing license plate detection methods, the proposed algorithm use a deep learning model to learn the features of the license plate. Experiment results demonstrate that our method can detect various types of license plates with a high accuracy and a relatively short running time.

关键词： license plate detection deep learning sparse auto-encoder

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Novelty Detection Using sparse auto-encoders to Characterize Structural Vibration Responses

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第10期47卷 13049-13062页

作者： Finotti, Rafaelle Piazzaroli Barbosa, Flavio de Souza Cury, Alexandre Abrahao Pimentel, Roberto Leal Univ Fed Juiz de Fora Grad Program Computat Modeling Juiz De Fora Brazil Univ Fed Juiz de Fora Grad Program Civil Engn Juiz De Fora Brazil Univ Fed Paraiba Grad Program Civil & Environm Engn Joao Pessoa Paraiba Brazil

Deep learning techniques have been increasingly popular for detecting structural novelties in recent years. The deep learning notion originates from the theory of neural networks, and it comprises several machine learning approaches that were primarily created to solve high-dimensional and nonlinear problems due to their great data mapping capabilities. Although the basic ideas of such algorithms were established in the 1960s, their use in damage detection situations is still relatively new. In so doing, the current study assesses the sparse auto-encoder (SAE) deep learning method when applied to the characterization of structural anomalies. The fundamental concept is to employ the SAE to extract significant features from monitored signals and the well-known Support Vector Machine (SVM) to classify those features within the framework of a Structural Health Monitoring (SHM) program. The proposed method is evaluated using vibration data from a numerical beam model and a highway viaduct in Brazil. The results demonstrate that the SAE can extract relevant properties from dynamic data, making it valuable for SHM applications.

关键词： Vibration monitoring Machine learning sparse auto-encoder Computational intelligence Damage detection Structural health monitoring

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