Super-Resolution Person Re-Identification With Semi-Coupled Low-Rank Discriminant Dictionary Learning

Jing, Xiao‐Yuan; Zhu, Xiaoke; Wu, Fei; Hu, Ruimin; You, Xinge; Wang, Yunlong; Feng, Hui; Yang, Jingyu

doi:10.1109/tip.2017.2651364

Cited by 145 publications

(120 citation statements)

References 45 publications

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“…Then, for P frames, both short-term (denoted by m s ) and long-term predictions could be selected by referring to the adjacent frames or background reference, respectively. As proven by Reference [3], a high-quality background reference can help reduce the bitrate of blocks in P frames.…”

Section: Encoding and Decoding Schemementioning

confidence: 99%

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Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing

Xiao

Zhu

et al. 2018

Remote Sensing

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City surveillance enables many innovative applications of smart cities. However, the real-time utilization of remotely sensed surveillance data via unmanned aerial vehicles (UAVs) or video satellites is hindered by the considerable gap between the high data collection rate and the limited transmission bandwidth. High efficiency compression of the data is in high demand. Long-term background redundancy (LBR) (in contrast to local spatial/temporal redundancies in a single video clip) is a new form of redundancy common in Earth observatory video data (EOVD). LBR is induced by the repetition of static landscapes across multiple video clips and becomes significant as the number of video clips shot of the same area increases. Eliminating LBR improves EOVD coding efficiency considerably. First, this study proposes eliminating LBR by creating a long-term background referencing library (LBRL) containing high-definition geographically registered images of an entire area. Then, it analyzes the factors affecting the variations in the image representations of the background. Next, it proposes a method of generating references for encoding current video and develops the encoding and decoding framework for EOVD compression. Experimental results show that encoding UAV video clips with the proposed method saved an average of more than 54% bits using references generated under the same conditions. Bitrate savings reached 25-35% when applied to satellite video data with arbitrarily collected reference images. Applying the proposed coding method to EOVD will facilitate remote surveillance, which can foster the development of online smart city applications.

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Section: Encoding and Decoding Schemementioning

confidence: 99%

“…One excellent work is the incremental K-SVD method for spatial big data representation [1,2]. Another representative work is the low-rank dictionary [3,4]. However, as we focus on the representation of continuous data sequences in the pixel domain, these methods cannot be directly applied to compress the remote sensing video data.…”

Section: Introductionmentioning

confidence: 99%

Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing

Xiao

Zhu

et al. 2018

Remote Sensing

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“…Recently, some approaches have paid attention to LR problem, and proposed algorithms to address the resolution mismatching problem, such as JUDEA [Li et al, 2015], SLD 2 L [Jing et al, 2015] and SING [Jiao et al, 2018]. These methods assume that all gallery images are HR, and probe images are LR with a fixed down-sampling factor.…”

Section: Comparison With State-of-the-art Lr Methodsmentioning

confidence: 99%

“…Actually, a couple of works [Jing et al, 2015;Jiao et al, 2018] have paid attention to LR and resolution mismatching problem. They set the probe image as a LR one, while all the gallery images are typically HR with a uniform scale.…”

Section: Introductionmentioning

confidence: 99%

“…During the process of super-resolving, SR methods Johnson et al, 2016; supplement and recover the missing appearance information (Figure 1), so that LR gallery images and the HR probe image can be treated equally. The generative adversarial network (GAN) for image SR [Ledig et al, 2016], i.e., SR-GAN, offers an effective solution to image SR, and is also one of the most competitive SR approaches.…”

Section: Introductionmentioning

confidence: 99%

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Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification

Wang

Yang

et al. 2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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Person re-identification (REID) is an important task in video surveillance and forensics applications. Most of previous approaches are based on a key assumption that all person images have uniform and sufficiently high resolutions. Actually, various low-resolutions and scale mismatching always exist in open world REID. We name this kind of problem as Scale-Adaptive Low Resolution Person Re-identification (SALR-REID). The most intuitive way to address this problem is to increase various low-resolutions (not only low, but also with different scales) to a uniform high-resolution. SR-GAN is one of the most competitive image superresolution deep networks, designed with a fixed upscaling factor. However, it is still not suitable for SALR-REID task, which requires a network not only synthesizing high-resolution images with different upscaling factors, but also extracting discriminative image feature for judging person's identity. (1) To promote the ability of scale-adaptive upscaling, we cascade multiple SRGANs in series. (2) To supplement the ability of image feature representation, we plug-in a reidentification network. With a unified formulation, a Cascaded Super-Resolution GAN (CSR-GAN) framework is proposed. Extensive evaluations on two simulated datasets and one public dataset demonstrate the advantages of our method over related state-of-the-art methods.

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Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN

Dan

Shah

et al. 2018

Softw Pract Exp

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Summary Brain healthcare, when supported by Internet of Things, can perform online and accurate analysis of brain big data for the classification of multivariate Electroencephalogram (EEG), which is a prerequisite for the recent boom in neurofeedback applications and clinical practices. However, it remains a grand research challenge due to (1) the embedded intensive noises and the intrinsic nonstationarity determined by the evolution of brain states; and (2) the lack of a user‐friendly computing platform to sustain the complicated analytics. This study presents the design of an online EEG classification system aided by Cloud centering on a lightweight Convolutional Neural Network (CNN). The system incrementally trains the CNN on Cloud and enables hot deployment of the trained classifier without the need to restart the gateway to adapt to the users' needs. The classifier maintains a High Convolutional Layer to gain the ability of processing high‐dimensional EEG segments. The number of hidden layers is minimized to ensure the efficiency of training. The lightweight CNN adopts an “hourglass” block of fully connected layers to reduce the number of neurons quickly toward the output end. A case study of depression evaluation has been performed against raw EEG datasets to distinguish between (1) Healthy and Major Depression Disorder with an accuracy, sensitivity, and specificity of [98.59% ± 0.28%], [97.77% ± 0.63%], and [99.51% ± 0.19%], respectively; and (2) Effective and Noneffective treatment outcome with an accuracy, sensitivity, and specificity of [99.53% ± 0.002%], [99.50% ± 0.01%], and [99.58% ± 0.02%], respectively. The results show that the classification can be completed several magnitudes faster when EEG is collected on the gateway (several milliseconds vs. 4 seconds).

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Super-Resolution Person Re-Identification With Semi-Coupled Low-Rank Discriminant Dictionary Learning

Cited by 145 publications

References 45 publications

Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing

Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing

Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification

Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN

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