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Real time surveillance for low resolution and limited data scenarios: An image set classification approach

INFORMATION SCIENCES 2021年 580卷 578-597页

作者： Nadeem, Uzair Shah, Syed Afaq Ali Bennamoun, Mohammed Togneri, Roberto Sohel, Ferdous Univ Western Australia Dept Comp Sci & Software Engn 35 Stirling Hwy Crawley WA 6009 Australia Univ Western Australia Dept Elect Elect & Comp Engn 35 Stirling Hwy Crawley WA 6009 Australia Murdoch Univ Discipline Informat Technol 90 South St Murdoch WA 6150 Australia Edith Cowan Univ Sch Sci 270 Joondalup Dr Joondalup WA 6027 Australia

This paper proposes a novel image set classification technique based on the concept of linear regression. Unlike most other approaches, the proposed technique does not require any training. We represent the gallery image sets as subspaces in a high dimensional space. Class specific gallery subspaces are used to estimate regression models for each image in the test image set. Images of the test set are then projected onto the gallery subspaces. The residuals, calculated using the Euclidean distance between the original and the projected test images, are used as the distance metric. Three different strategies are devised to decide on the final class of the test image set. We extensively evaluated the proposed technique using both low resolution and noisy images and with less gallery data to assess the suitability of our technique for the tasks of surveillance and video-based face recognition. The experiments show that the proposed technique achieves superior classification accuracy and has a faster execution time compared with existing techniques, especially under the challenging conditions of low resolution and a limited amount of gallery and test data. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Image set classification linear regression classification Surveillance Video-based face recognition Set-based object recognition

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linear Discriminant regression classification for Face Recognition

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IEEE SIGNAL PROCESSING LETTERS 2013年第1期20卷 91-94页

作者： Huang, Shih-Ming Yang, Jar-Ferr Natl Cheng Kung Univ Inst Comp & Commun Engn Dept Elect Engn Tainan 70101 Taiwan

To improve the robustness of the linear regression classification (LRC) algorithm, in this paper, we propose a linear discriminant regression classification (LDRC) algorithm to boost the effectiveness of the LRC for face recognition. We embed the Fisher criterion into the LRC as a novel discriminant regression analysis method. The LDRC attempts to maximize the ratio of the between-class reconstruction error (BCRE) over the within-class reconstruction error (WCRE) to find an optimal projection matrix for the LRC such that the LRC on that subspace can achieve a high discrimination for classification. Then, the projected coefficients are executed by the LRC for face recognition. Extensive experiments carried out on the FERET and AR face databases show that the LDRC performs better than the related regression based algorithms and shows a promising ability for face recognition.

关键词： Face recognition linear discriminant regression classification linear regression classification

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Reconstructive discriminant analysis: A feature extraction method induced from linear regression classification

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NEUROCOMPUTING 2012年 87卷 41-50页

作者： Chen, Yi Jin, Zhong Nanjing Univ Sci & Technol Sch Comp Sci & Technol Nanjing 210094 Jiangsu Peoples R China

Based on linear regression, a novel method called reconstructive discriminant analysis (RDA) is developed for feature extraction and dimensionality reduction (DR). RDA is induced from linear regression classification (LRC). LRC assumes each class lies on a linear subspace and finds the nearest subspace for a given sample. But the original space cannot guarantee that the given sample matches its nearest subspace. RDA is designed to make the samples match their nearest subspaces. Concretely, RDA characterizes the intra-class reconstruction scatter as well as the inter-class reconstruction scatter, seeking to find the projections that simultaneously maximize the inter-class reconstruction scatter and minimize the intra-class reconstruction scatter. Actually, RDA can also be seen as another form of classical linear discriminant analysis (LDA) from the reconstructive view. The proposed method is applied to face and finger knuckle print recognition on the ORL, extended YALE-B, FERET face databases and the PolyU finger knuckle print database. The experimental results demonstrate the superiority of the proposed method. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Reconstructive discriminant analysis Feature extraction Dimensionality reduction linear discriminant analysis linear regression classification Face recognition Finger Knuckle print recognition

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Characterisation of friction stir welds by logistic regression using fractal and wavelet features

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ADVANCES IN MATERIALS AND PROCESSING TECHNOLOGIES 2019年第4期5卷 582-597页

作者： Padmapriya, N. Venkateswaran, N. Vijayalakshmi, K. Mallieswaran, G. K. Padmanabhan, R. SSN Coll Engn Dept Math Kalavakkam Tamil Nadu India SSN Coll Engn Dept Elect & Commun Engn Kalavakkam India VIT Univ Sch Mech & Bldg Sci Chennai Tamil Nadu India

This paper presents a method for automatic detection of defective welds based on feature extraction using fractal analysis and wavelet transform with Logistic regression classifier. The proposed methodology consists of pre-processing, Weld seam extraction, and feature extraction followed by classification process. The image obtained from the welded region is converted to grayscale, followed by binarisation under pre-processing. The process of Weld seam extracts the region of interest (ROI), from which features are obtained that describe the texture and irregularities present in the welded image. The simulation study analyses Friction Stir Welding images of two different aluminium grades (AA1100 with AA6061-T6) and that of brass and copper. In the experiments, a Logistic regression model to perform classification using the features is employed. This is the first work where Logistic regression has been applied for weld defect detection in Friction Stir Welding. Experimental results show the effectiveness of the proposed classification process which uses less number of powerful features. The classifier has achieved more than 97% accuracy, indicating potential use of the proposed method for the detection of defects in the friction stir welded images.

关键词： Friction stir welding fractal dimension lacunarity wavelet transform linear regression classification defect

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linear collaborative discriminant regression classification for face recognition

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2015年 31卷 312-319页

作者： Qu, Xiaochao Kim, Suah Cui, Run Kim, Hyoung Joong Korea Univ Grad Sch Informat Secur & Management Seoul 136713 South Korea

This paper proposes a novel face recognition method that improves Huang's linear discriminant regression classification (LDRC) algorithm. The original work finds a discriminant subspace by maximizing the between-class reconstruction error and minimizing the within-class reconstruction error simultaneously, where the reconstruction error is obtained using linear regression classification (LRC). However, the maximization of the overall between-class reconstruction error is easily dominated by some large class-specific between-class reconstruction errors, which makes the following LRC erroneous. This paper adopts a better between-class reconstruction error measurement which is obtained using the collaborative representation instead of class-specific representation and can be regarded as the lower bound of all the class-specific between-class reconstruction errors. Therefore, the maximization of the collaborative between-class reconstruction error maximizes each class-specific between-class reconstruction and emphasizes the small class-specific between-class reconstruction errors, which is beneficial for the following LRC. Extensive experiments are conducted and the effectiveness of the proposed method is verified. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Face recognition Feature extraction Dimensionality reduction Collaborative representation Sparse representation linear regression classification linear collaborative discriminant regression classification linear discriminant regression classification

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Improving the Performance of Spiral Local Binary Pattern using Edge Information

Improving the Performance of Spiral Local Binary Pattern usi...

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International Scientific and Technical Conference on Computer Science and Information Technologies

作者： Nihan Kazak Cercevik Mehmet Koc Department of Computer Engineering Bilecik Seyh Edebali University Bilecik Turkey Department of Electrical and Electronics Engineering Bilecik Seyh Edebali University Bilecik Turkey

In many texture recognition problems, Local Binary Pattern (LBP) is used as texture descriptor and has achieved outstanding performances. Because of the success on the texture analysis, it is widely studied by many researchers and many variants of LBP are proposed. Edges of an image carry important information that can be used to extract discriminative features. In this paper, we investigate the importance of the information derived using Spiral Local Binary Pattern (SLBP) on the edges of a texture image. Also, we combine this information with SLBP features extracted from the whole image. linear regression classification (LRC) is used for the classification process. Our experimental results in two face databases, namely CURet and UIUC texture databases, show that spiral topology-based LBP features extracted from the edges of an image has important discriminative information that improve classification accuracy.

关键词： Texture recognition Spiral local binary pattern Edge Detection linear regression classification

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Parameterless reconstructive discriminant analysis for feature extraction

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NEUROCOMPUTING 2016年 190卷 50-59页

作者： Huang, Pu Gao, Guangwei Nanjing Univ Posts & Telecommun Sch Comp Sci & Technol Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210023 Jiangsu Peoples R China

Reconstructive discriminant analysis (RDA) is an effective dimensionality reduction method that can match well with linear regression classification (LRC). RDA seeks to find projections that can minimize the intra-class reconstruction scatter and simultaneously maximize the inter-class reconstruction scatter of samples. However, RDA needs to select the k heterogeneous nearest subspaces of each sample to construct the inter-class reconstruction scatter and it is very difficult to predefine the parameter k in practical applications. To deal with this problem, we propose a novel method called parameterless reconstructive discriminant analysis (PRDA) in this paper. Compared to traditional RDA, our proposed RDA variant cannot only fit LRC well but also has two important characteristics: (1) the performance of RDA depends on the parameter k that requires manual turning, while ours is parameter-free, and (2) it adaptively estimates the heterogeneous nearest classes for each sample to construct the inter-class reconstruction scatter. To evaluate the performance of the proposed algorithm, we test PRDA and some other state-of-the-art algorithms on some benchmark datasets such as the FERET, AR and ORL face databases. The experimental results demonstrate the effectiveness of our proposed method. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Dimensionality reduction Feature extraction linear regression classification Reconstructive discriminant analysis Parameterless

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Multi-linear regression Coefficient Classifier for Recognition

Multi-linear Regression Coefficient Classifier for Recogniti...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Feng, Qingxiang Zhu, Qi Yuan, Chun Lee, Ivan Univ Macau Fac Sci & Technol Macau Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Jiangsu Peoples R China Tsinghua Univ Shenzhen Grad Sch Shenzhen Peoples R China Univ South Australia Sch IT & Math Sci Adelaide SA Australia

ISBN: (纸本)9781509006229

In this paper, a new classifier, called multiple linear regression coefficients (MLRC), is proposed for image recognition. linear regression classification (LRC) uses the linear combination of the class-model for classification. Sparse representation based classification (SRC) utilizes the global-model for classification. Based on the global-concept of SRC, mean representation classification (MRC) is proposed, which uses the mean vector of each class to constitute the global-model for classification. Motivated by LRC, MRC and SRC, this paper focuses on finding the better global-model for classification. MLRC firstly constitutes regression coefficient matrix and multiple sub-global-models. Afterwards, MLRC computes the weighted value with the coefficient matrix and solve the least square error with multiple sub-global-models for classification. The PolyU Finger-Knuckle-Print (FKP) and Multi-spectral palmprint (MSP) databases are used to assess the proposed classifier. Experimental results demonstrate that the proposed approach achieves a better recognition rate than the LRC, MRC, SRC and some state-of-the-art methods.

关键词： FKP Recognition MSP Recognition linear regression classification Sparse Representation

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Marginal Fisher regression classification for Face Recognition 16th

Marginal Fisher Regression Classification for Face Recogniti...

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16th Pacific-Rim Conference on Multimedia (PCM)

作者： Ji, Zhong Yu, Yunlong Pang, Yanwei Li, Yingming Zhang, Zhongfei Tianjin Univ Sch Elect Informat Engn Tianjin 300072 Peoples R China SUNY Binghamton Dept Comp Sci Binghamton NY 13902 USA

ISBN: (纸本)9783319240756;9783319240749

This paper presents a novel marginal Fisher regression classification (MFRC) method by incorporating the ideas of marginal Fisher analysis (MFA) and linear regression classification (LRC). The MFRC aims at minimizing the within-class compactness over the between-class separability to find an optimal embedding matrix for the LRC so that the LRC on that subspace achieves a high discrimination for classification. Specifically, the within-class compactness is measured with the sum of distances between each sample and its neighbors within the same class with the LRC, and the between-class separability is characterized as the sum of distances between margin points and their neighboring points from different classes with the LRC. Therefore, the MFRC embodies the ideas of the LRC, Fisher analysis and manifold learning. Experiments on the FERET, PIE and AR datasets demonstrate the effectiveness of the MFRC.

关键词： Face recognition Nearest subspace classification linear regression classification Manifold learning

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Global linear regression coefficient classifier for recognition

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OPTIK 2015年第21期126卷 3234-3239页

作者： Feng, Qingxiang Zhu, Xingjie Pan, Jeng-Shyang Harbin Inst Technol Shenzhen Grad Sch Innovat Informat Ind Res Ctr Shenzhen Peoples R China China Hua Rang Holdings Corp LTD Dev Ctr Beijing Peoples R China

In this paper, a novel classifier based on linear regression classification (LRC), called global linear regression coefficient (GLRC) classifier, is proposed for recognition. LRC classifier uses the test sample and the class subspace to calculate the distance which will be used for classification. GLRC classifier uses the test sample vector and whole train space (all the class subspaces) to calculate the global linear regression coefficient. Then GLRC computes the signed square sum of the linear regression coefficients belonging to the same class, and the result will be used for classification. A large number of experiments on Yale face database and AR face database are used to evaluate the proposed algorithm. The experimental results demonstrate that the proposed method achieves better recognition rate than LRC classifier, sparse representation based classification (SRC) classifier, Collaborative representation based classification (CRC) classifier and two phase test sample sparse representation (TPTSSR) classifier and so on. (C) 2015 Elsevier GmbH. All rights reserved.

关键词： linear regression classification Sparse representation based classification Collaborative representation based classification

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