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RISS 인기검색어
- 검색키워드
- 저자 : ( Cungang Wang ) (검색결과 4 건)
- 무료
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Few Samples Face Recognition Based on Generative Score Space
Wang, Bin,Wang, Cungang,Zhang, Qian,Huang, Jifeng Korean Society for Internet Information 2016 KSII Transactions on Internet and Information Syst Vol.10 No.12
Few samples face recognition has become a highly challenging task due to the limitation of available labeled samples. As two popular paradigms in face image representation, sparse component analysis is highly robust while parts-based paradigm is particularly flexible. In this paper, we propose a probabilistic generative model to incorporate the strengths of the two paradigms for face representation. This model finds a common spatial partition for given images and simultaneously learns a sparse component analysis model for each part of the partition. The two procedures are built into a probabilistic generative model. Then we derive the score function (i.e. feature mapping) from the generative score space. A similarity measure is defined over the derived score function for few samples face recognition. This model is driven by data and specifically good at representing face images. The derived generative score function and similarity measure encode information hidden in the data distribution. To validate the effectiveness of the proposed method, we perform few samples face recognition on two face datasets. The results show its advantages.
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Learning Free Energy Kernel for Image Retrieval
( Cungang Wang ),( Bin Wang ),( Liping Zheng ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.8
Content-based image retrieval has been the most important technique for managing huge amount of images. The fundamental yet highly challenging problem in this field is how to measure the content-level similarity based on the low-level image features. The primary difficulties lie in the great variance within images, e.g. background, illumination, viewpoint and pose. Intuitively, an ideal similarity measure should be able to adapt the data distribution, discover and highlight the content-level information, and be robust to those variances. Motivated by these observations, we in this paper propose a probabilistic similarity learning approach. We first model the distribution of low-level image features and derive the free energy kernel (FEK), i.e., similarity measure, based on the distribution. Then, we propose a learning approach for the derived kernel, under the criterion that the kernel outputs high similarity for those images sharing the same class labels and output low similarity for those without the same label. The advantages of the proposed approach, in comparison with previous approaches, are threefold. (1) With the ability inherited from probabilistic models, the similarity measure can well adapt to data distribution. (2) Benefitting from the content-level hidden variables within the probabilistic models, the similarity measure is able to capture content-level cues. (3) It fully exploits class label in the supervised learning procedure. The proposed approach is extensively evaluated on two well-known databases. It achieves highly competitive performance on most experiments, which validates its advantages.
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Few Samples Face Recognition Based on Generative Score Space
( Bin Wang ),( Cungang Wang ),( Qian Zhang ),( Jifeng Huang ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.12
Few samples face recognition has become a highly challenging task due to the limitation of available labeled samples. As two popular paradigms in face image representation, sparse component analysis is highly robust while parts-based paradigm is particularly flexible. In this paper, we propose a probabilistic generative model to incorporate the strengths of the two paradigms for face representation. This model finds a common spatial partition for given images and simultaneously learns a sparse component analysis model for each part of the partition. The two procedures are built into a probabilistic generative model. Then we derive the score function (i.e. feature mapping) from the generative score space. A similarity measure is defined over the derived score function for few samples face recognition. This model is driven by data and specifically good at representing face images. The derived generative score function and similarity measure encode information hidden in the data distribution. To validate the effectiveness of the proposed method, we perform few samples face recognition on two face datasets. The results show its advantages.
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A Stray Capacitances Model of Inductors with Partial Layer of Windings
Bingxin Xu,Zhan Shen,Chenglei Liu,Cungang Hu,Bi Liu,Long Jin,Jiangfeng Wang,Xin Li,Zhike Xu,Wu Chen,Xiaohui Qu,Zhixiang Zou 전력전자학회 2023 ICPE(ISPE)논문집 Vol.2023 No.-
With the high precision requirements of electronic devices, more accurate analysis and modelling of stray capacitance is required in order to reduce electromagnetic interference (EMI) from the stray capacitance in inductors. This paper proposes an improved analytical model of the stray capacitances of the inductor, which takes into account the capacitances between the windings and the central limb, the side limb and the yokes of the core. A general model of the stray capacitance with each winding as a complete layer is calculated, and the potential of the floating core is derived analytically. For the case of a partial winding layer near the side limb, calculations are derived and the stray capacitance changes are compared for different winding layers. Finally, the stray capacitance model of the partial layer is verified by finite element simulations and experimental results on the prototype.
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