Semantic preserving distance metric learning and applications

Yu, Jun; Tao, Dapeng; Li, Jonathan; Cheng, Jun

doi:10.1016/j.ins.2014.01.025

Cited by 112 publications

(40 citation statements)

References 56 publications

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“…Graph-based learning methods have been extensively proposed for image segmentation [1,9,10,18,37,53,54,56,59] and classification [49,[61][62][63] . Additionally, texture images have Table 1 Notation summary for the paper.…”

Section: Related Workmentioning

confidence: 99%

Texture recognition based on diffusion in networks

Gonçalves

Silva

Costa

et al. 2016

Information Sciences

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Section: Related Workmentioning

confidence: 99%

Texture recognition based on diffusion in networks

Gonçalves

Silva

Costa

et al. 2016

Information Sciences

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“…Due to the improvement on the diversity of representations, combining multiple types of features has achieved great success in many areas [35], [36], [37]. The combination methods can be generally grouped into three categories [36]: descriptorlevel fusion, kernel-level fusion [38] and decision-level fusion [39].…”

Section: B Feature Fusion For Rankingmentioning

confidence: 99%

Dense Invariant Feature-Based Support Vector Ranking for Cross-Camera Person Reidentification

Tan

Zheng

Liu

et al. 2018

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-Recently, support vector ranking has been adopted to address the challenging person re-identification problem. However, the ranking model based on ordinary global features cannot well represent the significant variation of pose and viewpoint across camera views. To address this issue, a novel ranking method which fuses the dense invariant features is proposed in this paper to model the variation of images across camera views. An optimal space for ranking is learned by simultaneously maximizing the margin and minimizing the error on the fused features. The proposed method significantly outperforms the original support vector ranking algorithm due to the invariance of the dense invariant features, the fusion of the bidirectional features and the adaptive adjustment of parameters. Experimental results demonstrate that the proposed method is competitive with state-of-the-art methods on two challenging datasets, showing its potential for real-world person re-identification.

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“…Unauthorized access service, abuse of authority for access to unauthorized services. (4) Traffic analysis, active or passive traffic analysis so as to obtain the information time, rate, length, source and destination to become harmful [6].…”

Section: Figure 2 the General Organization And Node Distribution Of mentioning

confidence: 99%

Research on the Design and Implementation of Smart Home System based on ARM and ZigBee Techniques with the Analysis of Data Transmission Security Enhancement Model

Wang¹,

Huang²

2016

Proceedings of the 6th International Conference on Electronic, Mechanical, Information and Management Society

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Abstract. In this paper, we combine the technique of ARM and ZigBee to design and implement the novel smart home system. Through analyzing the data transmission security enhancement model, we modify the traditional model with security concern. Each smart home system can be controlled by the server to facilitate access to residential property services, municipal services, such as weather forecast information. While using ZigBee as household interior various network can be easily will not have communication interface devices connected to the Internet gateway, does not need complex wiring or buy expensive appliances with communication interface. In the future research, we will numerically experiment our system and collect feedback for further optimization.

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Semantic preserving distance metric learning and applications

Cited by 112 publications

References 56 publications

Texture recognition based on diffusion in networks

Texture recognition based on diffusion in networks

Dense Invariant Feature-Based Support Vector Ranking for Cross-Camera Person Reidentification

Research on the Design and Implementation of Smart Home System based on ARM and ZigBee Techniques with the Analysis of Data Transmission Security Enhancement Model

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