Semi-supervised learning framework based on statistical analysis for image set classification

Yan, Wenzhu; Sun, Quansen; Sun, Huaijiang; Li, Yanmeng

doi:10.1016/j.patcog.2020.107500

Cited by 18 publications

(6 citation statements)

References 31 publications

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“…These algorithms learn to exploit robustness to stochastic perturbations caused by noise or randomness in data augmentation [ 24 , 25 , 26 ]. This idea has been recently applied by [ 27 ] for image set classification with success. The idea of integrating active method along with semi-supervised learning was also introduced in the scientific literature.…”

Section: Related Work On Active Semi-supervised Learningmentioning

confidence: 99%

On the Use of Deep Active Semi-Supervised Learning for Fast Rendering in Global Illumination

Constantin

Bigand

2020

J. Imaging

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Convolution neural networks usually require large labeled data-sets to construct accurate models. However, in many real-world scenarios, such as global illumination, labeling data are a time-consuming and costly human intelligent task. Semi-supervised learning methods leverage this issue by making use of a small labeled data-set and a larger set of unlabeled data. In this paper, our contributions focus on the development of a robust algorithm that combines active and deep semi-supervised convolution neural network to reduce labeling workload and to accelerate convergence in case of real-time global illumination. While the theoretical concepts of photo-realistic rendering are well understood, the increased need for the delivery of highly dynamic interactive content in vast virtual environments has increased recently. Particularly, the quality measure of computer-generated images is of great importance. The experiments are conducted on global illumination scenes which contain diverse distortions. Compared with human psycho-visual thresholds, the good consistency between these thresholds and the learning models quality measures can been seen. A comparison has also been made with SVM and other state-of-the-art deep learning models. We do transfer learning by running the convolution base of these models over our image set. Then, we use the output features of the convolution base as input to retrain the parameters of the fully connected layer. The obtained results show that our proposed method provides promising efficiency in terms of precision, time complexity, and optimal architecture.

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Section: Related Work On Active Semi-supervised Learningmentioning

confidence: 99%

On the Use of Deep Active Semi-Supervised Learning for Fast Rendering in Global Illumination

Constantin

Bigand

2020

J. Imaging

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“…This feature set can represent color histograms, textures or gradients information [4,16,19,30,31]. The ultimate goal of feature selection is to use the most appropriate features for classification [5]. One approach to feature selection is computing features around a limited number of pixels (i.e.…”

Section: Introductionmentioning

confidence: 99%

Transforming data from the image to the text domain: benign versus malignant micro-calcification classification

Suhail,

Zwiggelaar

2023

VAWKUM trans. comput. sci.

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In this paper we present a new approach for the classification of benign and malignant micro-calcification clusters by transforming data from the image to the text domain. A string representation is extracted from binary micro-calcification segmentation images. We extracted two different features from the strings and combined different machine learning techniques towards benign versus malignant classification. We evaluated our proposed method on the DDSM database and experimental results indicates a Classification Accuracy equal to 92%.

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“…Semisupervised learning is implemented by adding unlabeled samples to supervised algorithms. 17 Supervised learning includes classification algorithm and regression algorithm. Classification is used to predict category labels, and regression is used to predict a continuous value.…”

Section: Introductionmentioning

confidence: 99%

“…In some circumstances, the input might be only part valid or limited to specific feedback. Semisupervised learning is implemented by adding unlabeled samples to supervised algorithms 17 . Supervised learning includes classification algorithm and regression algorithm.…”

Section: Introductionmentioning

confidence: 99%

Application of visual mechanical signal detection and loading platform with super‐resolution based on deep learning

Ding

Zhao

Zhang

et al. 2022

Int J of Intelligent Sys

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A visual mechanical signal detection and loading platform with super-resolution based on deep learning is designed to improve the detection accuracy of mechanical signals. The visual mechanical signal detection and loading platform with super-resolution include three-dimensional (3D) biological force quantitative detection platform and the mechanical signal loading platform with 3D magnetic distortion and ultrahigh resolution. In the 3D biological force quantitative detection platform, four 3D force sensors are used to collect mechanical signals, and the improved fuzzy clustering fusion method is used to fuse the mechanical signals collected by 3D force sensors to improve the detection accuracy of mechanical signals. The mechanical signal loading platform of 3D magnetic distortion and ultrahigh resolution technology connects the 3D magnetic distortion instrument and microscope, collects images through high-speed scanning components and distorted magnetic field, reconstructs the collected images by deep learning method, obtains ultrahigh-resolution mechanical signal visual images, and triggers mechanical signal loading and release by synchronous interactive system. The consequences of the experiment demonstrate that the designed platform can display the super-

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Semi-supervised learning framework based on statistical analysis for image set classification

Cited by 18 publications

References 31 publications

On the Use of Deep Active Semi-Supervised Learning for Fast Rendering in Global Illumination

On the Use of Deep Active Semi-Supervised Learning for Fast Rendering in Global Illumination

Transforming data from the image to the text domain: benign versus malignant micro-calcification classification

Application of visual mechanical signal detection and loading platform with super‐resolution based on deep learning

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