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An effective and efficient parallel large-scale cross-media retrieval in mobile cloud network

MULTIMEDIA TOOLS AND APPLICATIONS 2024年第5期83卷 13821-13850页

作者： Jiang, Nan Zhuang, Yi Chiu, Dickson K. W. Zhejiang Univ Sch Med Affiliated Hangzhou Peoples Hosp 1 Zhejiang Peoples R China Zhejiang Gongshang Univ Sch Comp Sci & Technol Zhejiang Peoples R China Univ Hong Kong Fac Educ HKSAR Hong Kong Peoples R China

With the rapid growth of multimedia data (e.g., text, image, video, audio and 3D model, etc) in the web, there are a large number of media objects with different modalities in the multimedia documents such as webpages, which exhibit latent semantic correlation. As a new type of multimedia retrieval method, cross-media retrieval is becoming increasingly attractive, through which users can get the results with various media types with the same semantic information by submitting a retrieval of any media type. The explosive increasing of the number of media objects, however, makes it difficult for the traditional local standalone mode to process efficiently. So the powerful parallel processing capability of cloud computing is accommodated to facilitate the efficient large-scale cross-media retrieval. In this paper, based on a Multi-Layer-Cross-Reference-Graph(MLCRG) model, we propose an efficient parallel cross-media retrieval (PCMR) method in which two enabling techniques (i.e., 1) the adaptive cross-media data allocation algorithm and 2) the PCIndex scheme) are accommodated to effectively speedup the retrieval performance. To the best of our knowledge, there is little research on the parallel retrieval processing of the large-scale cross-media databases in the mobile cloud network. Extensive experiments are conducted to testify that our proposed PCIndex method outperform the three competitors (e.g., the PFAR (Mao et al, 22), the MBSR (retrieval 4(2):153-164, 42) and the SPECH (Knowl Based Syst 251(5):1-13, 40)) in terms of the effectiveness and efficiency, respectively.

关键词： Cross-media Cross reference graph Cross storage Data allocation High-dimensional indexing

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The MDE for automating schema transformations of NoSQL databases 12

The MDE for automating schema transformations of NoSQL datab...

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12th IEEE International Symposium on Signal, image, video, and Communications, ISIVC 2024

作者： Daali, Noussaiba Amghar, Souad Mouline, Salma Cherdal, Safae Mohammed V University in Rabat Lrit Faculty of Sciences in Rabat Morocco

ISBN: (纸本)9798350385267

In rapidly evolving data-driven environments, the integration of heterogeneous data sources remains crucial. However, this heterogeneity, inherent in NoSQL databases, presents a significant challenge due to the diversities of the data storage and retrieval methods. This paper presents an automated database schema transformation process into the unified notation proposed in [1] where we gave a NoSQL databases schema integration approach. The three NoSQL schemas considered in this paper are Cassandra, MongoDB, and Couchbase. We use the Model-Driven Engineering (MDE) techniques and the ATL language for each specific database. A case study focused on COVID-19 is used to validate our transformations. © 2024 IEEE.

关键词： Big data

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Data Ingestions as a Service (DIaaS): A Unified Interface for Heterogeneous Data Ingestion, Transformation, and Metadata Management for Data Lake

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IEEE ACCESS 2024年 12卷 156131-156145页

作者： Sreepathy, H. V. Rao, B. Dinesh Jaysubramanian, Mohan Kumar Rao, B. Deepak Manipal Acad Higher Educ Manipal Sch Informat Sci Manipal 576104 India MulticoreWare Frameworks & Tools Coimbatore 641049 India

Data ingestion tools are critical component of Data Lake. Existing data ingestion tools face challenges of handling large variety, formats, sources of data. There exists void for unified data ingestion interface to handle the above research problems. This study proposes an innovative and integrated framework for data ingestion in a data lake, addressing the challenges posed by heterogeneous data sources, formats, and metadata management. The framework comprises three novel modules: First Unified Data Integration Connectors (UDIC), which provide seamless connectivity and data retrieval capabilities from diverse sources including databases, data warehouses, file systems, cloud storage, and APIs;Second, Adaptive Data Variety Transformation (ADVT), a module that intelligently handles the transformation and processing of structured, semi-structured, and unstructured data types, ensuring efficient ingestion into the data lake;and third, Intelligent Metadata Management (IMM), a module that captures, stores, and manages metadata associated with the ingested data, offering advanced search, discovery, and enrichment functionalities. Comparative study corroborates features offered by the service with existing data ingestion tools to evaluate the novelty and significance of the study. Performance validation shows varying ingestion latencies across different data types: approximately 148.1 microseconds per record for structured data, 234.2 microseconds per record for semi-structured data, 65.6 microseconds per kilobyte (KB) for video data, and 42.7 microseconds per KB for image data. These results underscore the importance of considering data structure and size in optimizing ingestion processes. Overall, this research aims to revolutionize data ingestion in data lake environments by providing a unified solution for handling diverse data sources, formats, and metadata management.

关键词： Big Data applications Metadata Soft sensors Real-time systems Organizations Java Internet of Things Relational databases File systems Data lakes Data ingestion data lake data sources data formats unified interface data ingestion service big data

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The MDE for automating schema transformations of NoSQL databases

The MDE for automating schema transformations of NoSQL datab...

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International Symposium on Signal, image, video and Communications (ISIVC)

作者： Noussaiba Daali Souad Amghar Salma Mouline Safae Cherdal LRIT Faculty of Sciences in Rabat Mohammed V University in Rabat Morocco

ISBN: (数字)9798350385267

ISBN: (纸本)9798350385274

关键词： COVID-19 databases NoSQL databases Soft sensors Memory Model driven engineering

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video searching and retrieval using scene classification in multimedia databases 2

Video searching and retrieval using scene classification in ...

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2nd International Conference for Emerging Technology, INCET 2021

作者： Haloi, Pranabjyoti Bhuyan, M.K. Indian Institute of Technology Guwahati Department of Electronics and Electrical Engineering Guwahati India

ISBN: (纸本)9781728170299

image and video indexing techniques play a key role in content-based searching in multimedia databases. A novel efficient video similarity search approach for content-based video retrieval in a large storage device is proposed. A compressed domain feature is used to classify the scenes of a video sequence. The performance of the said feature in classifying the scenes is evaluated by considering different feature lengths. Subsequently, a novel searching algorithm is proposed based on entropy of the image. This method incorporates temporal information of the video during video retrieval. Additionally, the proposed system has the facility to query both by video and by image. The 92.3% accuracy of the proposed method exceeded that of the VA-file 81.0% and OVA-file 76.4% methods and the computational complexity was lower than the VA-file method. This demonstrates the efficiency and effectiveness of the proposed method. © 2021 IEEE.

关键词： Classification (of information)

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Event-Based Microservices With Apache Kafka Streams: A Real-Time Vehicle Detection System Based on Type, Color, and Speed Attributes

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IEEE ACCESS 2021年 9卷 83137-83148页

作者： Kul, Seda Tashiev, Isabek Sentas, Ali Sayar, Ahmet Kocaeli Univ Dept Comp Engn TR-41001 Kocaeli Turkey

The work presented in this paper proposes a novel approach to tracking a specific vehicle over the video streams published by the collaborating traffic surveillance cameras. In recent years, smart, effective transportation systems and intelligent traffic management applications are among the topics that have been given importance by various institutions. Developing a scalable, fault-tolerant, and resilient traffic monitoring system that retrieves video chunks with the desired query is challenging. For these challenging problems, stream processing and data retrieval systems have been developed over the years. However, there are still existing shortcomings between users and retrieval systems. This paper investigates the problem of retrieving video chunks by key-value query based on publish/subscribe model. Thus, we propose a hybrid of an asynchronous and synchronous communication mechanism for the Event-Based Microservice framework. We aim to develop generic techniques for better utilization of existing platforms. In the proposed framework, (i) first of all, microservices detect vehicles and extract their type, color, and speed features, and stored them in the metadata repository. (ii) Microservices publish each feature as events (iii) Other microservices self-join subscribe to those events, which leads to more events being published by combing all the possibilities: type-color, type-speed, color-speed, and type-color-speed. Finally, (iv) the system visualizes the query result and system status in real-time. When the user has selected color or/and a type or/and a speed feature, the system will return the best-matched vehicles without re-processing the videos. Experimental results show that our proposed system filters messages in real-time and supports easy integration of new microservices with the existing system.

关键词： Streaming media image color analysis Real-time systems databases Feature extraction Support vector machines Publish-subscribe Intelligent transportation system (ITS) microservices publish-subscribe system stream computing vehicle detection

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Deep multi-query video retrieval

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Journal of Visual Communication and image Representation 2022年 85卷

作者： Akbacak, Enver Vural, Cabir Engineering Faculty Computer Engineering Haliç University Güzeltepe Mahallesi 15 Temmuz Şehitler Caddesi Eyüp/IstanbulNo: 15 34060 Turkey Engineering Faculty Department of Electrical and Electronics Engineering Marmara University Aydinevler Mah. Maltepe/Istanbul34854 Turkey

video retrieval methods have been developed for a single query. Multi-query video retrieval problem has not been investigated yet. In this study, an efficient and fast multi-query video retrieval framework is developed. Query videos are assumed to be related to more than one semantic. The framework supports an arbitrary number of video queries. The method is built upon using binary video hash codes. As a result, it is fast and requires a lower storage space. Database and query hash codes are generated by a deep hashing method that not only generates hash codes but also predicts query labels when they are chosen outside the database. The retrieval is based on the Pareto front multi-objective optimization method. Re-ranking performed on the retrieved videos by using non-binary deep features increases the retrieval accuracy considerably. Simulations carried out on two multi-label video databases show that the proposed method is efficient and fast in terms of retrieval accuracy and time. © 2022 Elsevier Inc.

关键词： Database systems

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Vehicle Number Plate Recognition and Information Access System

Vehicle Number Plate Recognition and Information Access Syst...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Pradeep B. Mane Varsha Kiran Patil Mayur R. Paithankar Mallikarjun M. Kotgire Samveg S. Shah Dept. Electronics and Telecommunication AISSMS Institute of Information Technology Pune India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

Addressing the growing challenges posed by increasing vehicles and stringent traffic violations, many vehicle number plates go undetected while passing through toll booths and traffic lights. Thus it creates the need for a vehicle number plate recognition system. Existing systems employing ANPR algorithms utilize MATLAB and K-Nearest neighbour algorithms for image processing. Drawbacks include limitations in adapting to dynamic environments, Real time recognition, handling multiple vehicles, slow processing speeds, incapability of multiple object detection in single frame. The paper focuses on Automatic Number Plate Recognition (ANPR), employing specialized cameras and advanced image processing and using YOLOv8 for object detection and extracting license plate information from video feeds using EasyOCR. The noisy data generated during character recognition is filtered out using Regex based algorithm. The system integrates with centralized databases for real-time vehicle information access for users. The same database is used to automate e-challan updation, contributing to traffic management by swiftly issuing fines for rule violations. The proposed video-based processing system achieves number plate detection using YOLOv8 and storage of vehicle number plates in a centralized MongoDB database with an accuracy achieved of 96% in license plate detection and reduces noisy data using algorithm. It also enables real-time data retrieval through a user interface using Reactjs as frontend and is also connected to the MongoDB database for precise and user-friendly access in modern traffic systems.

关键词： image recognition databases Heuristic algorithms Object detection Filtering algorithms Information filters Real-time systems Character recognition Noise measurement License plate recognition

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Optimal Projection Guided Transfer Hashing for image retrieval

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR video TECHNOLOGY 2020年第10期30卷 3788-3802页

作者： Zhang, Lei Liu, Ji Yang, Yang Huang, Fuxiang Nie, Feiping Zhang, David Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Northwestern Polytech Univ Ctr Opt Imagery Anal & Learning Xian 710072 Peoples R China Chinese Univ Hong Kong Shenzhen Sch Sci & Engn Hong Kong Peoples R China

Recently, learning to hash has been widely studied for image retrieval thanks to the computation and storage efficiency of binary codes. Most existing learning to hash methods have yielded significant performance. However, for most existing learning to hash methods, sufficient training images are required and used to learn precise hashing codes. In some real-world applications, there are not always sufficient training images in the domain of interest. In addition, some existing supervised approaches need a amount of labeled data, which is an expensive process in terms of time, labor and human expertise. To handle such problems, inspired by transfer learning, we propose a simple yet effective unsupervised hashing method named Optimal Projection Guided Transfer Hashing (GTH) where we borrow the images of other different but related domain i.e., source domain to help learn precise hashing codes for the domain of interest i.e., target domain. In GTH, we aim to learn domain-invariant hashing functions. To achieve that, we propose to minimize the error matrix between two hashing projections of target and source domains. We seek for the maximum likelihood estimation (MLE) solution of the error matrix between the two hashing projections due to the domain gap. Furthermore, an alternating optimization method is adopted to obtain the two projections of target and source domains. By doing so, two projections can be progressively aligned. Extensive experiments on various benchmark databases for cross-domain visual recognition verify that our method outperforms many state-of-the-art learning to hash methods. The source code is available at https://***/liuji93/GTH.

关键词： Binary codes Maximum likelihood estimation Training image retrieval Semantics Optimization methods Benchmark testing Projection alignment maximum likelihood estimation transfer learning learning to hash image retrieval

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Multimedia processing using deep learning technologies, high-performance computing cloud resources, and Big Data volumes

Multimedia processing using deep learning technologies, high...

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Concurrency Computation

作者： Mahmoudi, Sidi Ahmed Belarbi, Mohammed Amin Mahmoudi, Saïd Belalem, Ghalem Manneback, Pierre Computer Science Unit Faculty of Engineering UMONS Mons Belgium Computer Science Department Faculty of Exact and Applied Science University of Oran 1 Oran Algeria

The last few years have been marked by the presence of very large sets of images and videos in our everyday lives. These multimedia objects have a very fast frequency of creation and sharing since images and videos can come from different devices such as smartphones, satellites, cameras, or drones. They are generally used to illustrate objects in different situations (public areas, train stations, hospitals, political and sport events and competitions, etc). As consequence, image and video processing algorithms have got increasing importance for several computer vision applications that should be adapted for managing large-scale volumes and exploiting high performance computing resources (local or cloud). In this work, we propose a cloud-based toolbox (platform) for computer vision applications. This platform integrates a toolbox of image and video processing algorithms that can (i) exploit high performance computing cloud resources, (ii) execute applications in real time, and (iii) manage large-scale database using Big Data technologies. The related libraries and hardware drivers are automatically integrated and configured in order to offer to users an access to the different applications without the need to download, install, and configure software or hardware. Experiments were conducted using three kinds of applications: (i) image and video processing applications, (ii) deep learning techniques for images classification and multiobject localization, and (iii) images indexation and retrieval. These experiments demonstrated the interest of our platform for sharing, in an efficient way, our scientific contributions and annotated databases in order to improve the quality and performance of computer vision applications. © 2020 John Wiley & Sons, Ltd.

关键词： Deep learning Application programs Big data Cloud computing Computer hardware Computer vision Information management Multimedia systems video signal processing Computer vision applications High performance computing High performance computing resources image and video processing images classification images indexation and retrieval Multimedia processing Scientific contributions

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