Prior Knowledge-Based Probabilistic Collaborative Representation for Visual Recognition

Lan, Rushi; Zhou, Yicong; Liu, Zhenbing; Luo, Xiangfeng

doi:10.1109/tcyb.2018.2880290

Cited by 77 publications

(30 citation statements)

References 41 publications

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“…In this section, the extensive experiments on several face databases and some real numerical UCI data sets are conducted. In the experiments, we compare the proposed DCCRC with the state-of-the-art RBC methods including SRC [1], CRC [2], CCRC [46], Co-CRC [47], DSRC [44], ProCRC [22], and EProCRC [43]. It should be noted that all regularized parameters in the competing methods are preset as the range [10 − 3 , 10 − 2 , .…”

Section: Methodsmentioning

confidence: 99%

“…In many latest extensions of CRC, the class discrimination information of data in fact was fully employed for strengthening the power of the pattern classification [42][43][44][45][46][47]. From the point of view of probability, a probabilistic CRC (ProCRC) was developed by using the discriminative regularization of the representations between all the classes and each class [22].…”

Section: Introductionmentioning

confidence: 99%

“…From the point of view of probability, a probabilistic CRC (ProCRC) was developed by using the discriminative regularization of the representations between all the classes and each class [22]. Using the prior information of data the extended ProCRC (EProCRC) was proposed in [43], and using the coarse to fine representation the two-phased ProCRC was proposed in [33].…”

Section: Introductionmentioning

confidence: 99%

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Discriminative, Competitive, and Collaborative Representation-Based Classification with l2-Norm Regularizations

et al. 2019

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Recently, collaborative representation-based classification (CRC) and its many variations have been widely applied for various classification tasks in pattern recognition. To further enhance the pattern discrimination of CRC, in this article we propose a novel extension of CRC, entitled discriminative, competitive, and collaborative representation-based classification (DCCRC). In the proposed DCCRC, the class discrimination information is fully utilized for promoting the true class of each testing sample to dominantly represent the testing sample during collaborative representation. The class discrimination information is well considered in the newly designed discriminative l2-norm regularization that can decrease the ability of representation from the interclasses of each testing sample. Simultaneously, a competitive l2-norm regularization is introduced to the DCCRC model with the class discrimination information with the aim of enhancing the competitive ability of representation from the true class of each testing sample. The effectiveness of the proposed DCCRC is explored by extensive experiments on the several public face databases and some real numerical UCI data sets. The experimental results demonstrate that the proposed DCCRC achieves the superior performance over the state-of-the-art representation-based classification methods.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discriminative, Competitive, and Collaborative Representation-Based Classification with l2-Norm Regularizations

et al. 2019

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“…For larger feature maps, we assigned a more accurate anchor box to the target. By taking 12 different sizes of anchor boxes to predict faces of different scales, the sizes were (12,16), (16,24), (21,32), (24,41), (24,51), (33,51), (28,62), (39,64), (35,74), (44, 87), (53, 105), (64, 135). When the original YOLOV3 had three scales, it could predict a total of 3549 bounding boxes.…”

Section: Improved Prediction Layer Scalementioning

confidence: 99%

“…Face detection is a major issue in target detection. Many scholars have made significant progress in related fields [27][28][29]. For faces of different sizes, Guo et al [30] proposed MSFD, which is a multi-scale face detector in the reception domain and can detect faces of different scales.…”

Section: Introductionmentioning

confidence: 99%

SE-IYOLOV3: An Accurate Small Scale Face Detector for Outdoor Security

et al. 2020

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Small scale face detection is a very difficult problem. In order to achieve a higher detection accuracy, we propose a novel method, termed SE-IYOLOV3, for small scale face in this work. In SE-IYOLOV3, we improve the YOLOV3 first, in which the anchorage box with a higher average intersection ratio is obtained by combining niche technology on the basis of the k-means algorithm. An upsampling scale is added to form a face network structure that is suitable for detecting dense small scale faces. The number of prediction boxes is five times more than the YOLOV3 network. To further improve the detection performance, we adopt the SENet structure to enhance the global receptive field of the network. The experimental results on the WIDERFACEdataset show that the IYOLOV3 network embedded in the SENet structure can significantly improve the detection accuracy of dense small scale faces.

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A two‐stage privacy protection mechanism based on blockchain in mobile crowdsourcing

et al. 2021

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With the rise of the Internet of Things (IoT) and fifth‐generation (5G) networks, which have led to a surge in data processing and increased data transfer time, traditional cloud computing could no longer meet the needs of workers, so edge computing has emerged. Edge computing could meet the demand for low time consumption by processing data at the edge of the network and then transmitting it to a third‐party platform. However, since the credibility of the third‐party platform is unknown which can easily leak the privacy of workers. For the transparent mechanism of blockchain, a two‐stage privacy protection mechanism based on blockchain is proposed to solve this problem. In the first stage, this paper proposes a double disturbance localized differential privacy (DDLDP) algorithm to disturb the location information of workers. In the second stage, all the sensing data are uploaded to the blockchain through edge nodes, processed by the edge cloud, and fed back to the requester. Blockchain technology not only guarantees the integrity of sensing data, but also prevents the possibility of third‐party platforms from leaking workers' privacy. Through extensive performance evaluation and comparative experiments on real data sets, the DDLDP algorithm could effectively protect the privacy of workers and has higher service quality and data availability.

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Prior Knowledge-Based Probabilistic Collaborative Representation for Visual Recognition

Cited by 77 publications

References 41 publications

Discriminative, Competitive, and Collaborative Representation-Based Classification with l2-Norm Regularizations

Discriminative, Competitive, and Collaborative Representation-Based Classification with l2-Norm Regularizations

SE-IYOLOV3: An Accurate Small Scale Face Detector for Outdoor Security

A two‐stage privacy protection mechanism based on blockchain in mobile crowdsourcing

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