- 标题
- 摘要
- 关键词
- 实验方案
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A review on graph-based semi-supervised learning methods for hyperspectral image classification
摘要： In this article, a comprehensive review of the state-of-art graph-based learning methods for classification of the hyperspectral images (HSI) is provided, including a spectral information based graph semi-supervised classification and a spectral-spatial information based graph semi-supervised classification. In addition, related techniques are categorized into the following sub-types: (1) Manifold representation based Graph Semi-supervised Learning for HSI Classification (2) Sparse representation based Graph Semi-supervised Learning for HSI Classification. For each technique, methodologies, training and testing samples, various technical difficulties, as well as performances, are discussed. Additionally, future research challenges imposed by the graph-based model are indicated.
关键词： Image classification,Hyperspectral images,Semi-supervised learning,Graph-based learning
更新于2025-09-23 15:23:52
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Optical Character Recognition System for Nastalique Urdu-Like Script Languages using Supervised Learning
摘要： There are two main techniques to convert written or printed text into digital format. The first technique is to create an image of written/printed text, but images are large in size so they require huge memory space to store, also text in image form cannot be further processed like edit, search, copy etc. The second technique is to use an Optical Character Recognition (OCR) system. OCR’s can read documents and convert manual text documents into digital text and this digital text can be processed to extract knowledge. A huge amount of Urdu language’s data is available in handwritten or in printed form that needs to be converted into digital format for knowledge acquisition. Highly cursive, complex structure, bi-directionality, and compound in nature etc. make the Urdu language too complex to obtain accurate OCR results. In this study supervised learning based OCR system is proposed for Nastalique Urdu language. The proposed system’s evaluations under variety of experimental settings apprehend 98.4 % accuracy, which is highest recognition rate ever achieved by any Urdu language OCR system. The proposed system is simple to implement especially in software front of OCR system also the proposed technique is useful for printed text as well as handwritten text and it will help for developing more accurate Urdu OCR’s software systems in the future.
关键词： Optical Character Recognition (OCR),Image Processing,Urdu Nastalique,Supervised Learning,Pattern Recognition
更新于2025-09-23 15:23:52
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Learning Illuminant Estimation from Object Recognition
摘要： In this paper we present a deep learning method to estimate the illuminant of an image. Our model is not trained with illuminant annotations, but with the objective of improving performance on an auxiliary task such as object recognition. To the best of our knowledge, this is the first example of a deep learning architecture for illuminant estimation that is trained without ground truth illuminants. We evaluate our solution on standard datasets for color constancy, and compare it with state of the art methods. Our proposal is shown to outperform most deep learning methods in a cross-dataset evaluation setup, and to present competitive results in a comparison with parametric solutions.
关键词： Illuminant estimation,deep learning,convolutional neural networks,computational color constancy,semi-supervised learning
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Fully Convolutional Semi-Supervised Gan for Polsar Classification
摘要： We propose a novel semi-supervised fully convolutional network for Polarimetric synthetic aperture radar (PolSAR) terrain classification. First, by designing a fully convolutional structure, we can perform pixel-based classification tasks. Then, by applying semi-supervised generative adversarial networks (GANs), we utilize both labeled and unlabeled samples and aim to obtain higher classification accuracy. Through a mini-max two-player game, GAN has better performance than other “single-player” classifiers. Finally, we combine the fully convolutional structure with the semi-supervised GAN. Our fully convolutional semi-supervised GAN (FC-SGAN) has excellent spatial feature learning ability and can perform end-to-end pixel-based classification tasks. Experimental results show that compared with existing works, the proposed method has better performances. Even when the training set gets smaller, our method keeps high accuracy.
关键词： terrain classification,fully convolutional network,generative adversarial network,semi-supervised learning
更新于2025-09-23 15:21:21
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A flexible piezoelectric strain sensor array with laser-patterned serpentine interconnects
摘要： This paper evaluates the classification of multisample problems, such as electromyographic (EMG) data, by making aggregate features available to a per-sample classifier. It is found that the accuracy of this approach is superior to that of traditional methods such as majority vote for this problem. The classification improvements of this method, in conjunction with a confidence measure expressing the per-sample probability of classification failure (i.e., a hazard function) is described and measured. Results are expected to be of interest in clinical decision support system development.
关键词： Bayes methods,machine learning,statistical learning,pattern analysis,decision support systems,supervised learning
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 16th India Council International Conference (INDICON) - Rajkot, India (2019.12.13-2019.12.15)] 2019 IEEE 16th India Council International Conference (INDICON) - Performance Comparison Between Bipolar and Unipolar Switching Scheme for a Single-Phase Inverter Based Stand-alone Photovoltaic System
摘要： Self-organizing network (SON) mechanisms reduce operational expenditure in cellular networks while enhancing the offered quality of service. Within a SON, self-healing aims to autonomously solve problems in the radio access network and to minimize their impact on the user. Self-healing comprises automatic fault detection, root cause analysis, fault compensation, and recovery. This paper presents a root cause analysis system based on fuzzy logic. A genetic algorithm is proposed for learning the rule base. The proposed method is adapted to the way of reasoning of troubleshooting experts, which ease knowledge acquisition and system output interpretation. Results show that the obtained results are comparable or even better than those obtained when the troubleshooting experts define the rules, with the clear benefit of not requiring the experts to define the system. In addition, the system is robust, since fine tuning of its parameters is not mandatory.
关键词： genetic algorithms,self-organizing networks (SONs),Fuzzy systems,troubleshooting,root cause analysis,self-healing,supervised learning
更新于2025-09-23 15:21:01
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Supervised Non-negative Matrix Factorization Methods for MALDI Imaging Applications
摘要： Motivation: Non-negative matrix factorization (NMF) is a common tool for obtaining low-rank approximations of non-negative data matrices and has been widely used in machine learning, e.g., for supporting feature extraction in high-dimensional classification tasks. In its classical form NMF is an unsupervised method, i.e. the class labels of the training data are not used when computing the NMF. However, incorporating the classification labels into the NMF algorithms allows to specifically guide them towards the extraction of data patterns relevant for discriminating the respective classes. This approach is particularly suited for the analysis of mass spectrometry imaging (MSI) data in clinical applications, such as tumor typing and classification, which are amongst the most challenging tasks in pathology. Thus, we investigate algorithms for extracting tumor specific spectral patterns from MSI data by NMF methods. Results: In this paper, we incorporate a priori class labels into the NMF cost functional by adding appropriate supervised penalty terms. Numerical experiments on a MALDI imaging dataset confirm that the novel supervised NMF methods lead to significantly better classification accuracy and stability as compared to other standard approaches.
关键词： MALDI imaging,tumor typing,Non-negative matrix factorization,mass spectrometry imaging,supervised learning
更新于2025-09-23 15:21:01
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[IEEE 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) - Chengdu, China (2018.7.15-2018.7.18)] 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) - Spectral-Spatial Graph Convolutional Networks for Semel-Supervised Hyperspectral Image Classification
摘要： Collecting label samples is quite costly and time consuming for hyperspectral image (HSI) classification tasks. Semi-supervised learning framework, which combines the intrinsic information of labeled and unlabeled samples can alleviate the deficient labeled samples and increase the accuracy of HSI classification. In this paper, we propose a novel framework for semi-supervised learning on multiple spectral-spatial graphs that is based on graph convolutional networks (SGCN). In the filtering operation on graphs we consider the spatial information and spectral signatures of HSI simultaneously. The experimental results on three real-life HSI data sets, i.e. Botswana Hyperion, Kennedy Space Center, and Indian Pines, show that the proposed SGCN can significantly improve the classification accuracy. For instance, the over accuracy on Indian Pine data is increased from 78% to 93%.
关键词： Hyperspectral image classification,Graph fourier transform,Graph convolutional,Neural networks,Semi-supervised learning
更新于2025-09-23 15:21:01
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Semi-supervised convolutional neural networks with label propagation for image classification
摘要： Over the past several years, deep learning has achieved promising performance in many visual tasks, e.g., face verification and object classification. However, a limited number of labeled training samples existing in practical applications is still a huge bottleneck for achieving a satisfactory performance. In this paper, we integrate class estimation of unlabeled training data with deep learning model which generates a novel semi-supervised convolutional neural network (SSCNN) trained by both the labeled training data and unlabeled data. In the framework of SSCNN, the deep convolution feature extraction and the class estimation of the unlabeled data are jointly learned. Specifically, deep convolution features are learned from the labeled training data and unlabeled data with confident class estimation. After the deep features are obtained, the label propagation algorithm is utilized to estimate the identities of unlabeled training samples. The alternative optimization of SSCNN makes the class estimation of unlabeled data more and more accurate due to the learned CNN feature more and more discriminative. We compared the proposed SSCNN with some representative semi-supervised learning approaches on MINIST and Cifar-10 databases. Extensive experiments on landmark databases show the effectiveness of our semi-supervised deep learning framework.
关键词： convolutional neural network,semi-supervised learning,label propagation
更新于2025-09-23 15:21:01
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Post-Loaded Substrate-Integrated Waveguide Bandpass Filter With Wide Upper Stopband and Reduced Electric Field Intensity
摘要： This paper evaluates the classification of multisample problems, such as electromyographic (EMG) data, by making aggregate features available to a per-sample classifier. It is found that the accuracy of this approach is superior to that of traditional methods such as majority vote for this problem. The classification improvements of this method, in conjunction with a confidence measure expressing the per-sample probability of classification failure (i.e., a hazard function) is described and measured. Results are expected to be of interest in clinical decision support system development.
关键词： Bayes methods,machine learning,statistical learning,pattern analysis,decision support systems,supervised learning
更新于2025-09-23 15:19:57