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sparse representation classification With Structured Dictionary Design Strategy for Rotating Machinery Fault Diagnosis

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IEEE ACCESS 2021年 9卷 10012-10024页

作者： Kong, Yun Wang, Tianyang Qin, Zhaoye Chu, Fulei Tsinghua Univ Dept Mech Engn State Key Lab Tribol Beijing 100084 Peoples R China

Fault diagnosis technique is the core of Prognostics and Health Management (PHM) system, which plays a crucial role in the intelligent operation and maintenance of various rotating machineries. In this paper, we present a novel sparse representation classification framework with structured dictionary design strategy (SRC-SDD) for intelligent fault diagnosis of rotating machineries. The proposed SRC-SDD method consists of two stages, i.e., the structured dictionary design stage and the sparsity-based intelligent diagnosis stage. In the first stage, the novelty of SRC-SDD lies in the overlapping segmentation strategy for structured dictionary design, which leverages the structured prior knowledge of rotating machinery vibration signals, namely, the periodic self-similarity and shift-invariance properties. In the second stage, SRC-SDD achieves fault recognitions of testing samples using a sparsity-based diagnosis strategy based on the minimum sparse reconstruction error. The proposed structured dictionary design strategy can enhance the representation power of dictionaries and thus promote the recognition performance of the sparsity-based diagnosis strategy. Finally, the effectiveness of SRC-SDD has been validated on the gearbox fault dataset from IEEE PHM society. The diagnosis results show that SRC-SDD achieves the excellent recognition accuracy of 100% for predicting six different gearbox health states. Further, the comparative studies with three conventional SRC methods prove the superiority of SRC-SDD in terms of both the recognition performance and computation efficiency.

关键词： Dictionaries Fault diagnosis Machinery Pattern recognition Feature extraction Prognostics and health management Machine learning Fault diagnosis periodic self-similarity rotating machinery sparse representation classification structured dictionary design

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sparse representation classification of Tobacco Leaves Using Near-Infrared Spectroscopy and a Deep Learning Algorithm

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ANALYTICAL LETTERS 2018年第7期51卷 1029-1038页

作者： Zhang, Jianqiang Liu, Weijuan Hou, Ying Qiu, Changgui Yang, Shuangyan Li, Changyu Nie, Linru Yunnan Reascend Tobacco Technol Grp Co Ltd Kunming 650000 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Sci Kunming Yunnan Peoples R China Yunnan Comtestor Co Kunming Yunnan Peoples R China

A spare representation classification method for tobacco leaves based on near-infrared spectroscopy and deep learning algorithm is reported in this paper. All training samples were used to make up a data dictionary of the sparse representation and the test samples were represented by the sparsest linear combinations of the dictionary by sparse coding. The regression residual of the test sample to each class was computed and finally assigned to the class with the minimum residual. The effectiveness of spare representation classification method was compared with K-nearest neighbor and particle swarm optimization-support vector machine algorithms. The results show that the classification accuracy of the proposed method is higher and it is more efficient. The results suggest that near-infrared spectroscopy with spare representation classification algorithm may be an alternative method to traditional methods for discriminating classes of tobacco leaves.

关键词： Deep learning algorithm near-infrared spectroscopy sparse representation classification

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Frame level sparse representation classification for speaker verification

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第20期76卷 21211-21224页

作者： Hasheminejad, Mohammad Farsi, Hassan Univ Birjand Dept Elect & Comp Engn Shahid Avini Highway Birjand Iran

In this paper, we analyze the application of the sparse representation of frames of the speech signal for the speaker verification. It is lately shown that sparse representation classification (SRC), is promising for speaker recognition. We bring evidence that the frame level sparse representation classification resembles process of speech recognition in human sensory system. Since the recognition of different voices (noises) helps individuals to immediately distinguish between the noise and the original speech signal, a noise aware system was designed. As a principal in the sparse representation, we argued the mutual coherence of the dictionary columns, called dictionary atoms, which is not efficiently considered in the already published SRC base speaker verification researches. To suppress the mutual coherence, we use a dictionary learning method to construct a dictionary with effective atoms. Our proposed Frame Level sparse representation classification (FSRC), provides new insights to the SRC based speaker verification. We demonstrate that, in the SRC based speaker verification, using a dictionary whose atoms are orthogonal can be more extensible than a dictionary whose atoms are highly correlated, and that the mutual coherence suppression is even more effective than imposing strong orthogonality on the dictionary atoms. We consider the performance of state-of-the-art speaker recognition systems and the proposed method on NIST SRE 2004 data. Experimental results show that in comparison to baseline methods, when we have enough amount of information in the registration of targets, the proposed method improves the performance of speaker verification system in noisy conditions.

关键词： Speaker recognition Speaker verification sparse representation classification Frame level sparse representation classification

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Dictionary learning feature space via sparse representation classification for facial expression recognition

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ARTIFICIAL INTELLIGENCE REVIEW 2019年第1期51卷 1-18页

作者： Sun, Zhe Hu, Zheng-ping Wang, Meng Zhao, Shu-huan Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Peoples R China Taishan Univ Sch Phys & Elect Engn Tai An 271021 Shandong Peoples R China

Facial expression recognition (FER) plays a significant role in human-computer interaction. In this paper, adopting a dictionary learning feature space (DLFS) via sparse representation classification (SRC), we propose a method for FER. First, we obtain a difference dictionary (DD) from the feature space by indirectly using an auxiliary neutral training set. Next, we use a dictionary learning algorithm to train the DD;this algorithm considers the samples from the DD are approximately symmetrical structure. Finally, we use SRC to represent and determine the label of each query sample. We then verify out proposed method from the perspective of training samples, dimension reduction methods and Gaussian noise variances using a variety of public databases. In addition, we compare our DLFS_SRC approach with DLFS_CRC and DLFS_LRC approaches on the Extended Cohn-Kanade (CK+) database to analyze recognition results. Our simulation experiments show that our proposed method achieved satisfying performance levels for FER.

关键词： Facial expression recognition Difference dictionary Dictionary learning sparse representation classification

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Nearest Centroid Neighbor Based sparse representation classification for Finger Vein Recognition

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IEEE ACCESS 2019年 7卷 5874-5885页

作者： Shazeeda, Shazeeda Rosdi, Bakhtiar Affendi Univ Sains Malaysia Sch Elect & Elect Engn Engn Campus Nibong Tebal 14300 Malaysia

In this paper, an efficient finger vein recognition algorithm based on the combination of the nearest centroid neighbor and sparse representation classification techniques (kNCN-SRC) is presented. The previously proposed recognition algorithms are mainly based on distance computation. In the proposed method, the distance, as well as the spatial distribution, are considered to achieve a better recognition rate. The proposed method consists of two stages: first, the k nearest neighbors of the test sample are selected based on the nearest centroid neighbor, and then, in the second stage, based on the selected number of closest nearest centroid neighbors (k), the test sample is classified by sparse representation. Findings from the proposed method kNCN-SRC demonstrated an increased recognition rate. This improvement can be attributed to the selection of the train samples, where the train samples are selected by considering the spatial and distance distribution. In addition, the complexity of SRC is reduced by reducing the number of train samples for the classification of the test sample by sparse representation and the processing speed of the proposed algorithm is significantly improved in comparison with the conventional SRC which is due to the reduced number of training samples. It can be concluded that the kNCN-SRC classification method is efficient for finger vein recognition. An increase in the recognition rate of 3.35%, 9.07%, 20.23%, and 0.81% is obtained for the proposed kNCN-SRC method in comparison with the conventional SRC for the four tested public finger vein databases.

关键词： Distance criterion finger vein recognition k-nearest centroid neighbor sparse representation classification spatial distribution

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Multimodal image fusion using sparse representation classification in tetrolet domain

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DIGITAL SIGNAL PROCESSING 2018年 79卷 9-22页

作者： Shandoosti, Hamid Reza Mehrabi, Adel Hamedan Univ Technol Dept Elect Engn Hamadan 65155 Iran

Multimodal medical image fusion, which aims at integrating different multimodal information into a single output, plays an important role in the clinical applicability of medical images such as noninvasive diagnosis and image-guided surgery. The main motivation of this study is to model the coefficient selection step of medical image fusion as a pattern recognition task. The proposed method first decomposes the source images by the tetrolet transform. Subsequently, different activity measures are used to extract salient features from patches of the tetrolet subbands. The features are then fed to the sparse un-mixing by variable splitting and augmented Lagrangian (SUnSAL) classifier. Coefficients to be incorporated into the fused image are chosen by the classifier. Finally, the cycle-spinning technique is exploited to avoid artifacts. Experimental results on three pairs of medical images validate the reliability and credibility of the proposed method in clinical applications when compared with four state-of-the-art fusion methods. More specifically, the proposed framework does not suffer from contrast reduction, color distortion and loss of fine details. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Multimodal image fusion Tetrolet transform Cycle-spinning sparse representation classification Activity level measurement

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Image Set representation and classification with Attributed Covariate-Relation Graph Model and Graph sparse representation classification

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NEUROCOMPUTING 2017年 226卷 262-268页

作者： Chen, Zhuqiang Jiang, Bo Tang, Jin Luo, Bin Anhui Univ Sch Comp Sci & Technol 111 Jiulong Rd Hefei Peoples R China

Image set representation and classification is an important problem in computer vision and pattern recognition area. It has been widely used in many computer vision applications. In this paper, a new image set representation and classification method has been proposed. The main contributions of this paper are twofold: (1) a new image set representation model, called attributed covariate-relation graph (ACRG), has been proposed for image set representation and modeling. ACRG aims to represent image set with an attributed graph model which involves both image features and their spatial structure simultaneously. (2) A new graph data based sparse representation and classification method, called Graph sparse representation classification (GSRC) has been proposed to achieve ACRG classification. Experimental results on several datasets demonstrate the benefits of the proposed ACRG representation and GSRC classification.

关键词： Image set representation classification Attributed relation graph sparse representation classification

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Improving execution time for supervised sparse representation classification of hyperspectral images using the Moore-Penrose pseudoinverse

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JOURNAL OF APPLIED REMOTE SENSING 2019年第2期13卷

作者： Arias, Fernando X. Sierra, Heidy Arzuaga, Emmanuel Lab Appl Remote Sensing Imaging & Photon Mayaguez PR 00681 USA Univ Puerto Rico Dept Elect & Comp Engn Mayaguez PR 00682 USA Univ Puerto Rico Dept Comp Sci & Engn Mayaguez PR 00682 USA

Hyperspectral images (HSIs) contain spectral information on the order of hundreds of different wavelengths, providing information beyond the visible range. Such spectral sensitivity is often used for the classification of objects of interest within a spatial scene in fields, such as studies of the atmosphere, vegetation and agriculture, and coastal environments. The classification task involves the processing of high-dimensional data which fuels the need for efficient algorithms that better use computational resources. classification algorithms based on sparse representation classification perform classification with high accuracy by incorporating all the relevant information of a given scene in a sparse domain. However, such an approach requires solving a computationally expensive optimization problem with time complexity Omega(n(2)). We propose a method that approximates the least squares solution of the sparse representation classification problem for HSIs using the Moore-Penrose pseudoinverse. The resulting time complexity of this approach reduces to O(n(2)). The impact on the classification accuracy and execution time is compared to the state-of-the-art methods for three varied datasets. Our experimental results show that it is possible to obtain comparable classification performance current methods, with as much as 82% of a reduction in execution time, opening the door for the adoption of this technology in scenarios where classification of high-dimensional data is time critical. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： sparse representation classification hyperspectral imaging remote sensing sparse signal processing compressive sensing

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Non-negative dictionary based sparse representation classification for ear recognition with occlusion

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NEUROCOMPUTING 2016年 171卷 540-550页

作者： Yuan, Li Liu, Wei Li, Yang Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Commun Univ China Sch Int Studies Beijing 100024 Peoples R China

By introducing an identity occlusion dictionary to encode the occluded part on the source image, sparse representation based classification has shown good performance on ear recognition under partial occlusion. However, large number of atoms of the conventional occlusion dictionary brings expensive computational load to the SRC model solving. In this paper, we propose a non-negative dictionary based sparse representation and classification scheme for ear recognition. The non-negative dictionary includes the Gabor features dictionary extracted from the ear images, and non-negative occlusion dictionary learned from the identity occlusion dictionary. A test sample with occlusion can be sparsely represented over the Gabor feature dictionary and the occlusion dictionary. The sparse coding coefficients are noted with non-negativity and much more sparsity, and the non-negative dictionary has shown increasing discrimination ability. Experimental results on the USTB ear database show that the proposed method performs better than existing ear recognition methods under partial occlusion based on SRC. (C) 2015 Elsevier B.V. All rights reserved.

关键词： sparse representation classification Non-negative occlusion dictionary Ear recognition with occlusion

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Finger vein recognition using mutual sparse representation classification

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IET BIOMETRICS 2019年第1期8卷 49-58页

作者： Shazeeda, Shazeeda Rosdi, Bakhtiar Affendi Univ Sains Malaysia Sch Elect & Elect Engn Engn Campus Nibong Tebal 14300 Pulau Pinang India

sparse representation classification (SRC) is one of the popular methods of classification in biometrics, in which the decision of class for the test sample was based on the class with minimum reconstruction error. As SRC is based on the sparsity of the images, a decision based on reconstruction error is not ideal. In this study, an efficient classification methodology for finger vein recognition called mutual SRC (MSRC) is proposed. MSRC classifies the test sample by a new decision rule which significantly improves the recognition rate of the conventional SRC. By this new decision rule, the classification of the test sample is not only based on the nearest sparse neighbour but also on determining the training sample which considers the test sample as its nearest neighbour (NN). In this method, the training set is selected based on reconstruction error for the test sample, then which training sample considers the test sample as its NN is identified by sparse representation. Increases of 4.67, 10.59, 26.82, and 3.44% in the recognition rates are observed for the proposed MSRC method when compared with conventional SRC using the four public finger vein database.

关键词： vein recognition image classification nearest neighbour methods image reconstruction image representation finger vein recognition MSRC sparse representation classification biometrics test sample minimum reconstruction error mutual SRC decision rule recognition rate nearest neighbour training set training sample public finger vein database

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