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Efficient Image Set Classification Using Linear Regression Based Image Reconstruction

Syed A. A. Shah, Uzair Nadeem, Mohammed Bennamoun, Ferdous Sohel, Roberto Togneri
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
We propose a novel image set classification technique using linear regression models.  ...  We estimate regression models for each test image using the class specific gallery subspaces. Images of the test set are then reconstructed using the regression models.  ...  The proposed technique is based on the concept of image reconstruction using Lin-ear Regression Classification (LRC) [20] and nearest subspace classification.  ... 
doi:10.1109/cvprw.2017.88 dblp:conf/cvpr/ShahNBST17 fatcat:uzh64jwtmrbsdkmbgtsd3wkskq

Efficient Image Set Classification using Linear Regression based Image Reconstruction [article]

Syed Afaq Ali Shah, Uzair Nadeem, Mohammed Bennamoun, Ferdous Sohel, Roberto Togneri
2017 arXiv   pre-print
We propose a novel image set classification technique using linear regression models.  ...  We estimate regression models for each test image using the class specific gallery subspaces. Images of the test set are then reconstructed using the regression models.  ...  The proposed technique is based on the concept of image reconstruction using Linear Regression Classification (LRC) [17] and nearest subspace classification.  ... 
arXiv:1701.02485v1 fatcat:ub2ur5klfbhp7o5chui3drugrq

Representation Learning with Deep Extreme Learning Machines for Efficient Image Set Classification [article]

Muhammad Uzair, Faisal Shafait, Bernard Ghanem, Ajmal Mian
2015 arXiv   pre-print
Efficient and accurate joint representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification.  ...  We learn the non-linear structure of image sets with Deep Extreme Learning Machines (DELM) that are very efficient and generalize well even on a limited number of training samples.  ...  Given a probe image set Xt, we first reconstruct each of its samples using the learned DELM models and then estimate its label based on the smallest reconstruction error.  ... 
arXiv:1503.02445v3 fatcat:2x3pddwt3jagldi2asogmgnvlq

Study of Image Classification Accuracy with Fourier Ptychography

Hongbo Zhang, Yaping Zhang, Lin Wang, Zhijuan Hu, Wenjing Zhou, Peter W. M. Tsang, Deng Cao, Ting-Chung Poon
2021 Applied Sciences  
Multiple linear regression shows a strong linear relationship between the results of image classification accuracy and image visual appearance quality based on PSNR and SSIM with multiple training datasets  ...  It is also found that the image classification accuracy of FPM reconstructed with higher resolution images is significantly different from using the lower resolution images under the lower numerical aperture  ...  Based on the regression model, it becomes feasible to infer the image classification accuracy directly based on PSNR and SSIM rather than through the intensive and time-consuming deep learning-based image  ... 
doi:10.3390/app11104500 fatcat:dlodggbspjht5ebctwyweeutju

Sparse representation using nonnegative curds and whey

Yanan Liu, Fei Wu, Zhihua Zhang, Yueting Zhuang, Shuicheng Yan
2010 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition  
In the first stage we consider a set of sparse and nonnegative representations of a test image, each of which is a linear combination of the images within a certain class, by solving a set of regressiontype  ...  Experiments on several benchmark face databases and Caltech 101 image dataset demonstrate the efficiency and effectiveness of our nonnegative curds and whey method.  ...  The two inseparate learned linear regression models encode similarity and disparity information useful for data classification.  ... 
doi:10.1109/cvpr.2010.5539934 dblp:conf/cvpr/LiuWZZY10 fatcat:lxcndeeqzbclniu7ykudym6v3q

Fast Image Interpolation via Random Forests

Jun-Jie Huang, Wan-Chi Siu, Tian-Rui Liu
2015 IEEE Transactions on Image Processing  
The underlying idea of this proposed work is to apply random forests to classify the natural image patch space into numerous subspaces and learn a linear regression model for each subspace to map the low-resolution  ...  image patch to high-resolution image patch.  ...  By using the linear regression models (e.g.  ... 
doi:10.1109/tip.2015.2440751 pmid:26054066 fatcat:wy4xbqfewrdrplpwm7zjxyhgbu

Weighted Direct Nonlinear Regression for Effective Image Interpolation

Jieying Zheng, Wanru Song, Yahong Wu, Feng Liu
2019 IEEE Access  
This paper proposes a learning-based image interpolation method based on weighted direct nonlinear regression.  ...  Dictionary learning combined with nearest neighbor searching based on the normalized correlation coefficient in the entire training set is proposed as a soft classification method.  ...  SETTINGS The training image set has a direct influence on the reconstruction quality of the interpolation.  ... 
doi:10.1109/access.2018.2890517 fatcat:4qssc5j56jet7ezruorssku6h4

Sparse Ordinal Logistic Regression and Its Application to Brain Decoding

Emi Satake, Kei Majima, Shuntaro C. Aoki, Yukiyasu Kamitani
2018 Frontiers in Neuroinformatics  
In the reconstruction procedure, it was assumed that a stimulus image can be represented by a linear combination of local image bases of multiple scales (1 × 1, 1 × 2, 2 × 1, and 2 × 2; Figure 3A).  ...  A presented image was reconstructed by first predicting the contrasts of image bases from brain activity and then optimally combining the image bases multiplied by the predicted contrasts.  ... 
doi:10.3389/fninf.2018.00051 pmid:30158864 fatcat:uvyoax6wzvcljb42y4tgnktopi

Sparse ordinal logistic regression and its application to brain decoding [article]

Emi Satake, Kei Majima, Shuntaro Aoki, Yukiyasu Kamitani
2017 bioRxiv   pre-print
Classification and regression models are respectively used to predict discrete and continuous variables of interest.  ...  SOLR also outperformed classification and linear regression models with the same type of sparseness, indicating the advantage of the modeling tailored to ordinal outputs.  ...  A presented image was reconstructed by first predicting the contrasts of image bases and then 289 optimally combining the image bases multiplied by the contrasts.  ... 
doi:10.1101/238758 fatcat:grqflkixrvew5e4ndlim4ecpfm

Performance of an Objective Fabric Pilling Evaluation Method

Junmin Zhang, Xungai Wang, Stuart Palmer
2010 Textile research journal  
In previous work, we established the principle of objective fabric pilling evaluation based on two-dimensional dual-tree complex wavelet transform (2DDTCWT) image reconstruction and non-linear classification  ...  This proof-ofprinciple work was performed using standard pilling test images.  ...  Acknowledgement The standard pilling test images presented in this paper are the copyright property of the WoolMark Company and reproduced with their permission.  ... 
doi:10.1177/0040517510361802 fatcat:47dgafbfwjhmlcxodhaht47744

Removal of Impulse Noise in Images by Means of the Use of Support Vector Machines [chapter]

H. Gómez-Moreno, S. Maldonado-Bascón, F. López-Ferreras, P. Gil-Jiménez
2003 Lecture Notes in Computer Science  
In this new approach we use the classification and the regression based on SVMs.  ...  By using the classifier we select the noisy pixels into the images and by using the regression we obtain a reconstruction value based on the neighboring pixels.  ...  For this change we use a regression procedure based on SVMs. In these processes we cannot simultaneously use all the pixels of the image due to the amount of calculations needed.  ... 
doi:10.1007/3-540-44869-1_68 fatcat:fbddcqposbbtzmzrsvqlwlz6ci

A Survey on Various Single Image Super Resolution Techniques

A.Haza rathaiah
2013 International Journal of Innovative Research in Science, Engineering and Technology  
The SR image approaches reconstruct a single higher-resolution image from a set of given lower-resolution images . There is a basic need for digital images of higher resolutions and quality.  ...  Super resolution methods which is generate high-resolution (HR) image from one or more low resolution images and various image quality metrics reviewed as measure the original image and reconstructed image  ...  an external a large collection of high resolution -Computational efficiency is decrease IEEE/ 2014 Single Image Super-Resolution Using Dictionary-Based Local Regression [4] Dictionary based  ... 
doi:10.15680/ijirset.2012.0102024 fatcat:t45xr2uapvcrdnzds7ldc37eta

Learning Hierarchical Decision Trees for Single-Image Super-Resolution

Jun-Jie Huang, Wan-Chi Siu
2017 IEEE transactions on circuits and systems for video technology (Print)  
Deep-learning-based SR methods have also emerged in the literature to pursue better SR results. In this paper, we propose to use a set of decision tree strategies for fast and highquality image SR.  ...  Both the classification process and the regression process take an extremely small amount of computation.  ...  We suggested that a combination of the classification process and regression process should be an efficient solution.  ... 
doi:10.1109/tcsvt.2015.2513661 fatcat:zgvowkbwrrfc3ksj3uzrx2zvw4

Applying Machine Learning Algorithms to Solve Inverse Problems in Electrical Tomography

Tomasz Rymarczyk, Grzegorz Kłosowski, N. Mastorakis, V. Mladenov, A. Bulucea
2018 MATEC Web of Conferences  
Moreover, an innovative method of denoising tomographic output images with the use of convolutional auto-encoders was presented.  ...  Thanks to the use of a convolutional structure composed of two autoencoders, a significant improvement in the quality of the output image from the ECT tomography was achieved.  ...  Multiple regression, General linear model), nonlinear regression (generalized linear and nonlinear models), regression trees (Classification and regression trees), CHAID, Neural networks, etc.  ... 
doi:10.1051/matecconf/201821002016 fatcat:35chqubsh5eqhokfdmbbhqqfkm

Meta-XGBoost for Hyperspectral Image Classification Using Extended MSER-Guided Morphological Profiles

Alim Samat, Erzhu Li, Wei Wang, Sicong Liu, Cong Lin, Jilili Abuduwaili
2020 Remote Sensing  
(DART), elastic net regression and parallel coordinate descent-based linear regression (linear) and random forest (RaF) boosters.  ...  in terms of the classification of hyperspectral images using advanced spectral-spatial features, especially from generalized classification accuracy and model training efficiency perspectives.  ...  The authors would also like to express their appreciation to the Hyperspectral Image Analysis group and the NCALM at the University of Houston for providing the second data sets used in this study.  ... 
doi:10.3390/rs12121973 fatcat:3ndgfudmhfctrgpwekbalgwr4a
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