Robust and sparse label propagation for graph-based semi-supervised classification

Hua, Zhiwen; Yang, Youlong

doi:10.1007/s10489-021-02360-z

Cited by 6 publications

(2 citation statements)

References 46 publications

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“…The detailed description of these are follows: HO-GCNMR [30], KFME [30], ELD [32], CLBP [32], MBP [24], 6x6 MB-LBP [24] and ELBP [24] attain the ACC of 90.60%, 86.10%, 90.72%, 81.57%, 52.75%, 69.25% and 68.77% on TG dt = 20. RSLP [31], SparseFME [31] and SLP [31] procure the ACC of 93.63%, 89.91% and 91.00% when TG dt = 10. 2D-DWT+LBP [33], 2D-DWT+HELBP [33], ESLLE [34] and SLLE [34] Deep learning [35], MWEE-C [36], MEESRC [36] and HQ [36] attain the ACC of 90.00%, 92.00%, 99.80%, 96.50% and 95.70% on TG dt = 32.…”

Section: Eyb Datasetmentioning

confidence: 99%

Radial Orthogonal Median LBP (ROM-LBP): A Discriminant Local Descriptor in Light Variations for Face Recognition

Karanwal

Diwakar

2023

JGEU

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LBP and majority of its variants performs extremely well in front of moderate light variations. But when light variations becomes severe then performance of LBP and its variants is not satisfactory. Therefore there is a need of the more promising and impressive descriptor which performs well in harsh light variations. To complement these LBP based descriptors the proposed work launches the novel descriptor for Face Recognition (FR) in harsh lightning variations. This proposed descriptor is called as Radial Orthogonal Median LBP (ROM-LBP). The main demerit of these LBP based descriptors is that they all consider the uniform coordination between the neighbors and center pixel. Which mean raw pixel intensity is used for the comparison with the center pixel. The proposed work eliminates this problem in the introduced descriptor ROM-LBP, by replacing the raw pixels intensity with the median of the radial points in each orthogonal position of the two separate groups. The generated median is then used for comparison with the center pixel. The respective codes obtained from both the groups are concatenated to form the ROM-LBP size. As region feature extraction is done therefore ROM-LBP develops the large feature size. To make more effective descriptor, the services of FLDA is used and then classification was conducted by SVMs. Experiments conducted on EYB and YB datasets demonstrates the ability of the proposed ROM-LBP against various LBP and non-LBP based descriptors.

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Section: Eyb Datasetmentioning

confidence: 99%

Radial Orthogonal Median LBP (ROM-LBP): A Discriminant Local Descriptor in Light Variations for Face Recognition

Karanwal

Diwakar

2023

JGEU

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“…However, this kind of method ignores the benefits of unlabeled data. Thus, semi-supervised learning methods are proposed to enhance the model performance with few labeled data and many unlabeled data [10][11][12]. Recently, semi-supervised learning methods have developed rapidly and achieved surprising success for image classification with only a few labeled samples [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Dual-Training-Based Semi-Supervised Learning with Few Labels

Wu,

Sun,

Chen

2024

Applied Sciences

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The continual expansion in the number of images poses a great challenge for the annotation of the data. Therefore, improving the model performance for image classification with limited labeled data has become an important problem to solve. To address the problem, we propose in this paper a simple and effective dual-training-based semi-supervised learning method for image classification. To enable the model to acquire more valuable information, we propose a dual training approach to enhance model training. Specifically, the model is trained with different augmented data at the same time with soft labels and hard labels, respectively. In addition, we propose a simple and effective weight generation method for generating the weight of samples during training to guide the model training. To further improve the model performance, we employ a projection layer at the end of the network to guide the self-learning of the model by minimizing the distance of features extracted from different layers. Finally, we evaluate the proposed approach on three benchmark image classification datasets. The experimental results demonstrate the effectiveness of our proposed approach.

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