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Semi-supervised local feature selection for data classification

Science China(Information Sciences) 2021年第9期64卷 127-138页

作者： Zechao LI Jinhui TANG School of Computer Science and Engineering Nanjing University of Science and Technology

Conventional feature selection methods select the same feature subset for all classes, which means that the selected features might work better for some classes than the others. Towards this end, this paper proposes a new semi-supervised local feature selection method(S2 LFS) allowing to select different feature subsets for different classes. According to this method, class-specific feature subsets are selected by learning the importance of features considering each class separately. In particular, the class labels of all available data are jointly learned under a consistent constraint over the labeled data, which enables the proposed method to select the most discriminative features. Experiments on six data sets demonstrate the effectiveness of the proposed method compared to some popular feature selection methods.

关键词： local feature selection label-specific feature semi-supervised learning data classification discriminative feature

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Learn Robust Pedestrian Representation Within Minimal Modality Discrepancy for Visible-Infrared Person Re-Identification

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Journal of Computer Science & Technology 2022年第3期37卷 641-651页

作者： Yu-Jie Liu Wen-Bin Shao Xiao-Rui Sun College of Computer Science and Technology China University of Petroleum(East China)Qingdao 266580China

Visible-infrared person re-identification has attracted extensive attention from the community due to its potential great application prospects in video *** are huge modality discrepancies between visible and infrared images caused by different imaging *** studies alleviate modality discrepancies by aligning modality distribution or extracting modality-shared features on the original ***,they ignore a key solution,i.e.,converting visible images to gray images directly,which is efficient and effective to reduce modality *** this paper,we transform the cross-modality person re-identification task from visible-infrared images to gray-infrared images,which is named as the minimal modality *** addition,we propose a pyramid feature integration network(PFINet)which mines the discriminative refined features of pedestrian images and fuses high-level and semantically strong features to build a robust pedestrian ***,PFINet first performs the feature extraction from concrete to abstract and the top-down semantic transfer to obtain multi-scale feature ***,the multi-scale feature maps are inputted to the discriminative-region response module to emphasize the identity-discriminative regions by the spatial attention ***,the pedestrian representation is obtained by the feature *** experiments demonstrate the effectiveness of PFINet which achieves the rank-1 accuracy of 81.95%and mAP of 74.49%on the multi-all evaluation mode of the SYSU-MM01 dataset.

关键词： person re-identification modality discrepancy discriminative feature

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Particle Recognition in Dust Imagery Using Fractional Calculus and discriminative features

Particle Recognition in Dust Imagery Using Fractional Calcul...

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第三十九届中国控制会议

作者： Zheng Wang Helin Zhang Dongyan Li School of Electric and Control Engineering Xi’an University of Science and Technology

The coal dust measurement technologies which had the poor stabilization, low accuracy and being difficult to save data don’t consider the coal dust physical characteristics in the coal mine safety environment. The application of the coal dust image recognition method was constrained. This paper is proposed to apply image processing techniques for studying coal particle classification measurement problem. The change regularity of coal dust characteristic parameters is thoroughly studied. The mathematical models of fractional order variational image denoising and coal dust image recognition are developed. The effects of regularization parameter and fractional order derivative on the coal dust image recognition parameters are discussed. The relationship between image parameters and coal dust characteristic parameters are determined. And the inner mechanisms of coal dust imagery analysis characteristic parameters are revealed. The simulation result indicated that the recognition error rate achieved 3.31% in particle size 250 m, 3.84% in(120 m, 250 m] and 5.41% in particle size 120 m, respectively. The proposed approach outperforms all other widely used approaches. The proposed approach can accurately identify particle groups, and has strong compatibility with images.

关键词： Dust particle Image recognition Fractional calculus discriminative feature

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