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نتیجه جستجو - Re-identification

تعداد مقالات یافته شده: 10
ردیف عنوان نوع
1 Recent developments of content-based image retrieval (CBIR)
پیشرفت های اخیر بازیابی تصاویر مبتنی بر محتوا (CBIR)-2021
With the development of Internet technology and the popularity of digital devices, Content-Based Image Retrieval (CBIR) has been quickly developed and applied in various fields related to computer vision and artificial intelligence. Currently, it is possible to retrieve related images effectively and efficiently from a large scale database with an input image. In the past ten years, great efforts have been made for new theories and models of CBIR and many effective CBIR algorithms have been established. In this paper, we present a survey on the fast developments and applications of CBIR theories and algorithms during the period from 2009 to 2019. We mainly review the technological developments from the viewpoint of image representation and database search. We further summarize the practical applications of CBIR in the fields of fashion image retrieval, person re-identification, e-commerce product retrieval, remote sensing image retrieval and trademark image retrieval. Finally, we discuss the future research directions of CBIR with the challenge of big data and the utilization of deep learning techniques.© 2020 Elsevier B.V. All rights reserved.
Keywords: Content-based image retrieval | Image representation | Database search | Computer vision | Big data | Deep learning
مقاله انگلیسی
2 Towards a self-sufficient face verification system
به سمت یک سیستم تأیید چهره خودکفا-2021
The absence of a previous collaborative manual enrolment represents a significant handicap towards designing a face verification system for face re-identification purposes. In this scenario, the system must learn the target identity incrementally, using data from the video stream during the operational authentication phase. So, manual labelling cannot be assumed apart from the first few frames. On the other hand, even the most advanced methods trained on large-scale and unconstrained datasets suffer performance degradation when no adaptation to specific contexts is performed. This work proposes an adaptive face verification system, for the continuous re- identification of target identity, within the framework of incremental unsupervised learning. Our Dynamic Ensemble of SVM is capable of incorporating non-labelled information to improve the performance of any model, even when its initial performance is modest. The proposal uses the self-training approach and is compared against other classification techniques within this same approach. Results show promising behaviour in terms of both knowledge acquisition and impostor robustness.
Keywords: Adaptive biometrics | Video surveillance | Video-to-video face verification | Unsupervised learning | Incremental learning
مقاله انگلیسی
3 AI-GAN: Asynchronous interactive generative adversarial network for single image rain removal
هوش مصنوعی -GAN: شبکه مواد تخاصمی ناهمزمان برای حذف باران با یک تصویر-2020
Single image rain removal plays an important role in numerous multimedia applications. Existing algo- rithms usually tackle the deraining problem by the way of signal removal, which lead to over-smoothness and generate unexpected artifacts in de-rained images. This paper addresses the deraining problem from a completely different perspective of feature-wise disentanglement, and introduces the interactions and constraints between two disentangled latent spaces. Specifically, we propose an Asynchronous Interactive Generative Adversarial Network (AI-GAN) to progressively disentangle the rainy image into background and rain spaces in feature level through a two-branch structure. Each branch employs a two-stage synthe- sis strategy and interacts asynchronously by exchanging feed-forward information and sharing feedback gradients, achieving complementary adversarial optimization. This ‘adversarial’ is not only the ‘adversar- ial’ between the generator and the discriminator, but also means that the two generators are entangled, and interact with each other in the optimization process. Extensive experimental results demonstrate that AI-GAN outperforms state-of-the-art deraining methods and benefits various typical multimedia applica- tions such as Image/Video Coding, Action Recognition, and Person Re-identification.
Keywords: Feature-wise disentanglement | Asynchronous and interactive | Single image deraining | Complementary adversarial training
مقاله انگلیسی
4 Multi-attention deep reinforcement learning and re-ranking for vehicle re-identification
یادگیری تقویتی عمیق چند منظوره و رتبه بندی مجدد برای شناسایی مجدد خودرو-2020
For solving the vehicle Re-identification (Re-ID) task, we need to focus our attention on the details with arbitrary size in the image, and it’s tough to locate these details accurately. In this paper, we propose a Multi-Attention Deep Reinforcement Learning (MADRL) model to focus on multi-attentional subregions that spreading randomly in the image, and extract the discriminative features for the Re-ID task. First, we obtain multiple attentions from the representative features, then group the feature channels into different parts, then train a deep reinforcement learning model to learn more accurate positions of these fine-grained details with different losses. Unlike existing models with complex strategies to keep the patch-matching constrains, our MADRL model can automatically locate the matching patches (multiattentional subregions) in different vehicle images with the same identification (ID). Furthermore, based on the fine-grained attention and global features we re-calculate the distance between the inter- and intra- classes, and we get better re-ranking results. Compared with state-of-the-art methods on three large-scale vehicle Re-ID datasets, our algorithm greatly improves the performance of vehicle Re-ID.
Keywords: Re-identification | Deep reinforcement learning | Multi-attention | Re-ranking
مقاله انگلیسی
5 When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey
وقتی سیستم های خودمختار با دقت و قابلیت انتقال از طریق هوش مصنوعی روبرو می شوند : بررسی-2020
With widespread applications of artificial intelligence (AI), the capabilities of the perception, understanding, decision-making, and control for autonomous systems have improved significantly in recent years. When autonomous systems consider the performance of accuracy and transferability, several AI methods, such as adversarial learning, reinforcement learning (RL), and meta-learning, show their powerful performance. Here, we review the learning-based approaches in autonomous systems from the perspectives of accuracy and transferability. Accuracy means that a well-trained model shows good results during the testing phase, in which the testing set shares a same task or a data distribution with the training set. Transferability means that when a well-trained model is transferred to other testing domains, the accuracy is still good. Firstly, we introduce some basic concepts of transfer learning and then present some preliminaries of adversarial learning, RL, and meta-learning. Secondly, we focus on reviewing the accuracy or transferability or both of these approaches to show the advantages of adversarial learning, such as generative adversarial networks, in typical computer vision tasks in autonomous systems, including image style transfer, image super-resolution, image deblurring/dehazing/rain removal, semantic segmentation, depth estimation, pedestrian detection, and person re-identification. We furthermore review the performance of RL and meta-learning from the aspects of accuracy or transferability or both of them in autonomous systems, involving pedestrian tracking, robot navigation, and robotic manipulation. Finally, we discuss several challenges and future topics for the use of adversarial learning, RL, and meta-learning in autonomous systems.
مقاله انگلیسی
6 روش یادگیری متخاصم عمیق و چند مرحله ای ، برای باز شناسی شخص مبتنی بر ویدئو
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 13 - تعداد صفحات فایل doc فارسی: 42
بازشناسی شخص (re-ID) بر مبنای ویدئو را میتوان به عنوان فرآیند تطبیق تصویر یک فرد از طریق دیدهای مختلف دوربین که به وسیله ی تصاویر ویدئویی ناهم راستا گرفته شده است، در نظر گرفت. روش هایی که برای اینکار وجود دارند، از سیگنال های نظارتی برای بهینه سازی فضای پیش روی دوربین استفاده نموده که تحت این شرایط، فاصله ی بین ویدئوها بیشینه سازی/کمینه سازی میشود. البته این کار باعث شده تا برچسب گذاری افراد در سطح دید های ویدئو بسیار زیاد شده و باعث شده تا نتوان آنها را به خوبی بر روی دوربین های شبکه بندی شده ی بزرگ مقیاس بندی کرد. همچنین خاطر نشان شده است که یادگیری نمایش های مختلف ویدئویی و آنهم به وسیله ی عدم تغییر دید دوربین را نمیتوان انجام داد چرا که ویژگی های تصویر، هر کدام دارای توزیع های مختلف مختص به خود میباشند. بنابراین تطبیق ویدئوها برای باز شناسی افراد، نیاز به مدل هایی انعطاف پذیر برای بدست آوردن پویایی های موجود در مشاهدات ویدئویی و یادگیری دیدهای ثابت از طریق دسترسی به نمونه های آموزشی برچسب دار و محدود دارد. در این مقاله قصد داریم یک روش مبتنی بر یادگیری عمیق چند مرحله ای را برای باز شناسی یک فرد بر مبنای ویدئو ارائه دهیم و بتوانیم به یادگیری دیدهای قابل قیاسی از این فرد که متمایز هستند بپردازیم. روش پیشنهادی را بر روی شبکه های عصبی باز رخداد گر متغیر (VRNN) توسعه داده ایم و آنرا به منظور ایجاد متغیر های پنهان با وابستگی های موقت که بسیار متمایز بوده ولی در تطبیق تصاویر فرد از نظر دید ثابت میباشد، مورد یادگیری قرار داده ایم. آزمایش های وسیعی را بر روی سه مجموعه ی داده ای بنچ مارک انجام داده ایم و به صورت تجربی به اثبات قابلیت روش پیشنهادی مان در ایجاد ویژگی های موقتی و با یک دید ثابت و کارائی بالایی که به وسیله ی آن بدست آمده است خواهیم پرداخت.
کلمات کلیدی: باز شناسی شخص مبتنی بر ویدئو | شبکه های عصبی باز رخدادگر متغیر | یادگیری متخاصم
مقاله ترجمه شده
7 A Deep and Structured Metric Learning Method for Robust Person Re-Identification
یک روش یادگیری متریک عمیق و ساختار یافته برای شناسایی مجدد شخص قدرتمند-2019
Person re-identification (re-ID) is to match different images of the same pedestrian. It has attracted in- creasing research interest in pattern recognition and machine learning. Traditionally, person re-ID is for- mulated as a metric learning problem with binary classification output. However, higher order relation- ship, such as triplet closeness among the instances, is ignored by such pair-wise based metric learning methods. Thus, the discriminative information hidden in these data is insufficiently explored. This paper proposes a new structured loss function to push the frontier of the person re-ID performance in realistic scenarios. The new loss function introduces two margin parameters. They operate as bounds to remove positive pairs of very small distances and negative pairs of large distances. A trade-offcoefficient is as- signed to the loss term of negative pairs to alleviate class-imbalance problem. By using a linear function with the margin-based objectives, the gradients w.r.t. weight matrices are no longer dependent on the iterative loss values in a multiplicative manner. This makes the weights update process robust to large iterative loss values. The new loss function is compatible with many deep learning architectures, thus, it induces new deep network with pair-pruning regularization for metric learning. To evaluate the perfor- mance of the proposed model, extensive experiments are conducted on benchmark datasets. The results indicate that the new loss together with the ResNet-50 backbone has excellent feature representation ability for person re-ID.
Keywords: Metric learning | Feature extraction | Deep neural networks | Imbalance regularization | Person re-identification
مقاله انگلیسی
8 Deep learning-based methods for person re-identification: A comprehensive review
روشهای مبتنی بر یادگیری عمیق برای شناسایی مجدد شخص: مرور جامع-2019
In recent years, person re-identification (ReID) has received much attention since it is a fundamental task in intelligent surveillance systems and has widespread application prospects in numerous fields. Given an image of a pedestrian captured from one camera, the task is to identify this pedestrian from the gallery set captured by other multiple cameras. It is a challenging issue since the appearance of a pedestrian may suffer great changes across different cameras. The task has been greatly boosted by deep learn- ing technology. There are mainly six types of deep learning-based methods designed for this issue, i.e. identification deep model, verification deep model, distance metric-based deep model, part-based deep model, video-based deep model and data augmentation-based deep model. In this paper, we first give a comprehensive review of current six types of deep learning methods. Second, we present the detailed descriptions of existing person ReID datasets. Then, some state-of-the-art performances of methods over recent years on several representative ReID datasets are summarized. Finally, we conclude this paper and discuss the future directions of the person ReID.
Keywords: Person re-identification | Deep learning | Literature review
مقاله انگلیسی
9 Privacy preserving data by conceptualizing smart cities using MIDR Angelization
حفظ حریم شخصی داده ها با مفهوم سازی شهرهای هوشمند با استفاده از MIDR Enhancing-2018
Smart City and IoT improves the performance of health, transportation, energy and reduce the consumption of resources. Among the smart city services, Big Data analytics is one of the imperative technologies that have a vast perspective to reach sustainability, enhanced resilience, effective quality of life and quick management of resources. This paper focuses on the privacy of big data in the context of smart health to support smart cities. Furthermore, the trade-off between the data privacy and utility in big data analytics is the foremost concern for the stakeholders of a smart city. The majority of smart city application databases focus on preserving the privacy of individuals with different disease data. In this paper, we propose a trust-based hybrid data privacy approach named as “MIDR-Angelization” to assure privacy and utility in big data analytics when sharing same disease data of patients in IoT industry. Above all, this study suggests that privacy-preserving policies and practices to share disease and health information of patients having the same disease should consider detailed disease information to enhance data utility. An extensive experimental study performed on a real-world dataset to measure instance disclosure risk which shows that the proposed scheme outperforms its counterpart in terms of data utility and privacy.
Keywords: Big data ، IoT data management ، Disclosure risk ، HIPAA ، Patient privacy ، Re-identification risk ، Smart city
مقاله انگلیسی
10 Semantic privacy-preserving framework for electronic health record linkage
چارچوب حفظ محتواي معنايي براي پيوند رکورد سلامت الکترونيک-2018
The combination of digitized health information and web-based technologies offers many possibilities for data analysis and business intelligence. In the healthcare and biomedical research domain, applications depending on electronic health records (EHRs) identify pri vacy preservation as a major concern. Existing solutions cannot always satisfy the evolving research demands such as linking patient records across organizational boundaries due to the potential for patient re-identification. In this work, we show how semantic methods can be applied to support the formulation and enforcement of access control policy whilst ensuring that privacy leakage can be detected and prevented. The work is illustrated through a case study associated with the Australasian Diabetes Data Network (ADDN – www.addn.org.au), the national paediatric type-1 diabetes data registry, and the Australian Urban Research Infrastructure Network (AURIN – www.aurin.org.au) platform that supports Australia-wide access to urban and built environment data sets. We demon strate that through extending the eXtensible Access Control Markup Language (XACML) with semantic capabilities, finer-grained access control encompassing data risk disclosure mechanisms can be supported. We discuss the contributions that can be made using this approach to socio-economic development and political management within business sys tems, and especially those situations where secure data access and data linkage is required.
مقاله انگلیسی
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