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Image Classification of multimedia Platforms With Texture algorithms and Neural Networks

IEEE access 2025年 13卷 46478-46494页

作者： Sauzameda-Gonzalez, Mauricio Alvarez-Borrego, Josue Guerra-Rosas, Esperanza Ctr Invest Cient & Educ Super Ensenada CICESE Ensenada 22860 Baja California Mexico Univ Autonoma Baja California Fac Ingn Arquitectura & Diseno Ensenada 22860 Baja California Mexico

In this paper, a methodology for real-time image classification on multimedia platforms has been developed. For this purpose, six feedforward neural network models were trained with images from two databases, which were preprocessed by three texture extraction methods: local binary pattern-uniform (LBP-U), gray level co-occurrence matrix (GLCM), and wavelet image scattering (WIS). The databases used consist of 157,448 images of the sections with the thumbnails of the platform content (mosaics) representing 14 classes and 38,214 images with the descriptions of the available content (descriptors) representing 11 classes, where all images have a resolution of 1280 x 720 pixels. The six models (three for mosaics and three for descriptors) were validated with images from the databases, which were not part of the training process, to obtain their performance metrics. The training and validation process was performed 30 times, and the average results were compared. The most outstanding models for each database were the neural networks trained with the wavelet image scattering method, with metrics of 99.97 +/- 0.01 % accuracy, 99.99 +/- 0.01 % specificity, 99.84 +/- 0.06 % sensitivity, 99.59 +/- 0.13 % precision and 99.71 +/- 0. 08 % of F1 score with a response time of 0.7349 seconds for the model trained with mosaics and with metrics of 99.90 +/- 0.03 % of accuracy, 99.94 +/- 0.02 % of specificity, 99.58 +/- 0.15 % of sensitivity, 98.63 +/- 0.55 % of precision and 99.09 +/- 0.30 % of F1 score with a response time of 0.6227 seconds for the model trained with descriptor images. The results are very significant due to the high efficiency obtained and confirm the effectiveness of the models with the WIS method for the classification of multimedia platform images with the characteristics of the databases used. It is suggested that the remaining methods be adjusted to improve their performance.

关键词： Gray-scale Mathematical models Symmetric matrices Scattering Real-time systems Image classification Statistical analysis Object recognition multimedia databases Histograms Classification method neural networks texture analysis methods local binary pattern grayscale co-occurrence matrix performance metrics

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Understanding the Impact of Wi-Fi Configuration on Volumetric Video Streaming Applications

Understanding the Impact of Wi-Fi Configuration on Volumetri...

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SIGCOMM Workshop on Emerging multimedia systems (EMS)

作者： Kulkarni, Umakant Diab, Khaled Aggarwal, Shivang Cao, Lianjie Ahmed, Faraz Sharma, Puneet Fahmy, Sonia Hewlett Packard Labs Palo Alto CA 94304 USA Purdue Univ W Lafayette IN USA

ISBN: (纸本)9798400703034

Emerging multimedia applications often use a wireless LAN (Wi-Fi) infrastructure to stream content. These Wi-Fi deployments vary vastly in terms of their system configurations. In this paper, we take a step toward characterizing the Quality of Experience (QoE) of volumetric video streaming over an enterprise-grade Wi-Fi network to: (i) understand the impact of Wi-Fi control parameters on user QoE, (ii) analyze the relation between Quality of Service (QoS) metrics of Wi-Fi networks and application QoE, and (iii) compare the QoE of volumetric video streaming to traditional 2D video applications. We find that Wi-Fi configuration parameters such as channel width, radio interface, access category, and priority queues are important for optimizing Wi-Fi networks for streaming immersive videos.

关键词： Wi-Fi Volumetric Video Streaming Quality of Service Quality of Experience access Category Channel Width Radio Interface Priority Queues

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A Comprehensive Study on Various Encryption Techniques for Digital Images in multimedia Networks 2

A Comprehensive Study on Various Encryption Techniques for D...

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2nd IEEE International Conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Santhosha, Benaka Sudheesh, K.V. Kiran, K. Parameshachari, B.D. Dayanand Sagar University Harohalli Bengaluru Visvesveraya Technological University Department of CSE Belagavi India Vidyavardhaka College of Enginerring Mysuru Visvesveraya Technological University Department of ECE Belagavi India Nitte Meenakshi Institute of Technology Department of ECE Bengaluru India

ISBN: (纸本)9798350317558

Image encryption is a fundamental component of modern data security that guarantees the integrity, privacy, and confidentiality of sensitive visual content. This paper provides a thorough examination of image encryption, comparing and contrasting different encryption techniques and their benefits and limitations as well as real-world uses. In order to strengthen image data security against unwanted access and manipulation, we first offer some basic understanding of image encryption. The suitability of symmetric, asymmetric, and hybrid encryption techniques in various contexts is examined. We also explore the assessment criteria used to evaluate encryption algorithms, highlighting the significance of using suitable measures to precisely gauge security and effectiveness. We also discuss common issues with image encryption, like complicated key management and computational complexity. The review also explores future directions that picture encryption might take, including multi-media encryption techniques, resistance to cryptanalysis, and quantum image encryption. We also highlight how important image encryption is in a variety of fields, such as finance, healthcare, journalism, intellectual property protection, and military operations. We will conduct a thorough analysis of current cryptography schemes and multimedia encryption algorithms to provide a comprehensive overview of the current security landscape tailored to digital multimedia technology. The findings from this survey will enhance our understanding of the efficacy and dependability of secure multimedia encryption schemes, ultimately aiding in the development of more efficient and robust encryption methods for the future. This article aims to summarize and assess numerous algorithms using different methodologies based on multiple characteristics such as MSE, PSNR, NC, BER, and so on. © 2024 IEEE.

关键词： Cryptography Image multimedia Networks Security Attacks Steganography Watermarking

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Assessment of the effects of 5G MEC cache on DASH adaptation algorithms

Assessment of the effects of 5G MEC cache on DASH adaptation...

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International Symposium on Broadband multimedia systems and Broadcasting (BMSB)

作者： Viola, Roberto Amendola, Danilo Fernandez, Zaloa Gabilondo, Alvaro Zorrilla, Mikel Casals, Mikel Angueira, Pablo Montalban, Jon Basque Res & Technol Alliance Vicomtech Fdn San Sebastian Spain Univ Navarra Tecnun San Sebastian Spain Univ Basque Country Dept Commun Engn Bilbao 48013 Spain Univ Basque Country Dept Elect Technol Bilbao 48013 Spain

ISBN: (纸本)9781665469012

video streaming services are the major sources of Internet traffic, and their usage is constantly growing. To cope with this increasing video traffic demand, 5G comes with a new Radio access Network (RAN), having increased network data rates, and the Multi-access Edge Computing (MEC) architecture, enabling the deployment of services closer to the end devices. 5G RAN and MEC open to new opportunities to enforce and boost the video content delivery, as the content can be cached locally at the MEC host and served with improved RAN connectivity. However, the presence of a cache located at MEC may affect player adaptation algorithm when employing Adaptive Bitrate (ABR) streaming technologies, such as Dynamic Adaptive Streaming over HTTP (DASH). The cache influences the network measurements performed by the DASH player, which are later used, among other factors, by its adaptation algorithm to select the video representation bitrate. This paper investigates the interactions between MEC cache and DASH adaptation algorithms in a real testbed, including 5G RAN and MEC platform. The effects on different adaptation algorithms are analyzed while caching the DASH video content at the MEC.

关键词： 5G Adaptive Streaming content Caching Multi-access Edge Computing & Quality of Service

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Deep Learning-Based Detection of Inappropriate Speech content for Film Censorship

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IEEE access 2022年 10卷 101697-101715页

作者： Wazir, Abdulaziz Saleh Ba Karim, Hezerul Abdul Lyn, Hor Sui Fauzi, Mohammad Faizal Ahmad Mansor, Sarina Lye, Mohd Haris Multimedia Univ Fac Engn Cyberjaya 63000 Malaysia

Audible content has become an effective tool for shaping one's personality and character due to the ease of accessibility to a huge audible content that could be an independent audio files or an audio of online videos, movies, and television programs. There is a huge necessity to filter inappropriate audible content of the easily accessible videos and films that are likely to contain an inappropriate speech content. With this in view, all the broadcasting and online video/audio platform companies hire a lot of manpower to detect the foul voices prior to censorship. The process has a large cost in terms of manpower, time and financial resources. In addition to inaccurate detection of foul voices due to fatigue of manpower and weakness of human visual and hearing system in long time and monotonous tasks. As such, this paper proposes an intelligent deep learning-based system for film censorship through a fast and accurate detection and localization approach using advanced deep Convolutional Neural Networks (CNNs). The dataset of foul language containing isolated words samples and continuous speech were collected, annotated, processed, and analyzed for the development of automated detection of inappropriate speech content. The results indicated the feasibility of the suggested systems by reporting a high volume of inappropriate spoken terms detection. The proposed system outperformed state-of-the-art baseline algorithms on the novel foul language dataset evaluation metrics in terms of macro average AUC (93.85%), weighted average AUC (94.58%), and all other metrics such as F1-score. Additionally, proposed acoustic system outperformed ASR-based system for profanity detection based on the evaluation metrics including AUC, accuracy, precision, and F1-score. Additionally, proposed system was proven to be faster than human manual screening and detection of audible content for films' censorship.

关键词： Speech recognition Acoustics Computational modeling Deep learning Motion pictures Censorship Speech processing Foul language speech recognition key word spotting spoken term detection censorship deep learning convolutional neural network

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Deep Learning-Driven Video Summarization on the Cloud: A Pathway to Efficient Storage and Quick access 16

Deep Learning-Driven Video Summarization on the Cloud: A Pat...

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16th IEEE International Symposium on Embedded Multicore/Many-core systems-on-Chip (MCSoC)

作者： Darwich, Mahmoud Khalil, Kasem Ismail, Yasser Bayoumi, Magdy Univ Mt Union Dept Math & Comp Sci Alliance OH 44601 USA Univ Mississippi Dept Elect & Comp Engn University MS 38677 USA Southern Univ A&M Coll Dept Elect Engn Baton Rouge LA 70807 USA Univ Louisiana Lafayette Dept Elect & Comp Engn Lafayette LA 70504 USA

ISBN: (纸本)9798350393613

Cloud-based video summarization is an increasingly popular technique for managing large video repositories more efficiently. In this research, we present a novel approach for cloud-based video summarization that focuses on reducing storage costs and improving accessibility by automatically extracting critical information from videos and generating brief summaries. Our approach leverages cloud computing technology and deep learning algorithms for video analysis and processing. We evaluate the effectiveness of our approach using a realworld dataset of video files and demonstrate that it can generate high-quality video summaries while significantly reducing storage requirements. Our proposed approach has potential applications in areas such as security monitoring, video surveillance, and multimedia content management. This paper contributes to the existing literature by presenting a comprehensive study of cloud-based video summarization and proposing a novel solution that addresses the limitations of existing approaches, thereby improving the efficiency and cost-effectiveness of video management.

关键词： Cloud computing Video summarization Deep learning Storage costs multimedia content management

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A Hybrid Approach to Recommending Universal Decimal Classification Codes for Cataloguing in Slovenian Digital Libraries

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IEEE access 2022年 10卷 85595-85605页

作者： Borovic, Mladen Ojstersek, Milan Strnad, Damjan Univ Maribor Fac Elect Engn & Comp Sci Lab Heterogeneous Comp Syst Maribor 2000 Slovenia Univ Maribor Fac Elect Engn & Comp Sci Lab Geospatial Modelling Multimedia & Artificial Maribor 2000 Slovenia

In this article we present a hybrid approach to recommending the Universal Decimal Classification (UDC) codes for unclassified documents. By recommending UDC codes to librarians, we can provide the decision support as part of a semi-automatic cataloguing process. As current approaches to recommending UDC codes are scarce and limited to certain fields of expertise, our motivation was to create a hybrid approach which covers all fields of expertise within the UDC hierarchy. The cascade hybrid approach combines the BM25 ranking function with a multi-label classifier based on a BERT (Bidirectional Encoder Representations from Transformers) deep neural network architecture. Additionally, lists of the most commonly used UDC codes within a document's origin organization are used as a final content-based filtering method. The BM25 ranking function is used to create an initial list of recommendations. The first cascade step re-ranks the initial list of recommendations using the list of recommendations produced by the multi-label BERT-based classifier. The second cascade step re-ranks the resulting recommendation list from the first cascade step using a list of most commonly used UDC codes within the document's organization. Finally, post-processing steps are applied to obtain the final list of recommended UDC codes. We present in detail the UDC structure, the used text corpus of documents and the functioning of our hybrid recommendation approach. We perform the analysis of the generated recommendation lists for the corpus from the Slovenian Open-access Infrastructure using the metrics hit-ratio, normalized discounted cumulative gain, mean reciprocal rank and mean average precision. Our hybrid recommendation approach improves reference scores of individual methods for these metrics.

关键词： Codes Libraries Recommender systems Filtering Measurement Computer science Labeling Classification algorithms Hybrid recommender systems multi-label classification digital libraries universal decimal classification

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An Enhanced Digital Technology to Analyze the Combined Effect of Artificial Intelligence and Big Data Analytics for multimedia Information Processing

An Enhanced Digital Technology to Analyze the Combined Effec...

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2024 International Conference on Smart systems for Electrical, Electronics, Communication and Computer Engineering, ICSSEEC 2024

作者： Naser, Zamen Latef Adile, Mohaned Hussain, Angham Khalid Hasan, Forat Falih Majed, Safa Mohammed, Mohammed Abdulamer Almusawi, Muntather National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Kitab University Department of Computer Engineering Technologies Kirkuk Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Hilla University College Babil Iraq College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350378177

For the past several years, taking very large collections of images in everyday life has become more common. All audiovisual artifacts are more likely to be shared and collaborated on since images might come from various devices such as smartphones, satellites, web cams, and drones. Not only that but they are often employed to represent things in a wide range of settings (public areas, train stations, hospitals, political and sports events, competitions, etc.). Hence, multimedia information processor approaches are growing in significance for the many kinds of visual identification jobs that must be adapted to handle large numbers while filling available window processing time. New approaches are being developed, audiences' tastes are shifting, and digital media technologies are becoming more powerful and accessible regularly, contributing to the medium's ever-evolving landscape. This article presents the planning stages of a MIPS (multimedia Information Processing System). This study provides a platform for image processing methods using a virtualized toolbox. Virtual servers, applications, system time, and data mining are all made possible on this platform thanks to the combination of these digital processing approaches. Additionally, the necessary frameworks and equipment adapters are automatically linked and installed, allowing members to access various apps without downloading and installing software programs. Experiments employed digital processing applications, (ii) a deep backpropagation algorithm for picture classification and multiobject placement, and (iii) a program for indexing and retrieving images. These analyses indicated the system's goal of facilitating the open distribution of important developments and annotation databases to guarantee the efficacy of artificial intelligence-based applications for processing multimedia content. © 2024 IEEE.

关键词： Big data

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Impact of Social Media on Self-esteem and Emotions: An Instagram-Based Case Study 18th

Impact of Social Media on Self-esteem and Emotions: An Insta...

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18th International Conference on Information for a Better World: Normality, Virtuality, Physicality, Inclusivity, iConference 2023

作者： Martínez-Cardama, Sara Gómez-López, Elena Department of Library and Information Sciences University Carlos III of Madrid Madrid Spain University Carlos III of Madrid Madrid Spain

ISBN: (纸本)9783031280313

Social networks currently serve not only as platforms for publishing content but also as fundamental tools for accessing information. This role in providing access is mediated by a series of opaque, algorithm-based mechanisms for personalising the content. This article draws on existing literature on the relationship between possible mental health disorders and the functioning of these platforms to try to understand their effects on elements such as self-esteem and emotions. To this end, it focuses on the Instagram social network, which is prominent in the user groups corresponding to the Millennial and Z generations due to its high visual and multimedia content, its capacity for uncovering trends, and its integration with social commerce. It presents the results of a study (n = 100) of Instagram users between the ages of 18 and 39. These results provide relevant data on patterns associated with the following: time spent on the platform and excessive use, the risk of emotional loneliness or isolation, displacement of daily activities, and feelings of inferiority. They also reveal a real lack of awareness of how the algorithms on these types of platforms work and an interest in the mechanisms of disconnection and digital well-being. Lastly, the results open up new possibilities for inclusion of these risks in digital literacy programmes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Social networking (online)

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Vis2Rec: A Large-Scale Visual Dataset for Visit Recommendation 23

Vis2Rec: A Large-Scale Visual Dataset for Visit Recommendati...

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23rd IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

作者： Soumm, Michael Popescu, Adrian Delezoide, Bertrand Amanda 34 Ave Champs Elysees F-75008 Paris France Univ Paris Saclay CEA List F-91120 Palaiseau France

ISBN: (纸本)9781665493468

Most recommendation datasets for tourism are restricted to one world region and rely on explicit data such as checkins. However, in reality, tourists visit various places worldwide and document their trips primarily through photos. These images contain a wealth of raw information that can be used to capture users' preferences and recommend personalized content. Visual content was already used in past works, but no large-scale publicly-available dataset that gives access to users' personal images exists for recommender systems. As such a resource would open-up possibilities for new image-based recommendation algorithms, we introduce Vis2Rec, a new dataset based on visit data extracted from users' Flickr photographic streams, which includes over 7 million photos, 36k recognizable points of interest, and 14k user profiles. Google Landmarks v2 is used as an auxiliary dataset to identify points of interest in users' photos, using a state-of-the-art image-matching deep architecture. Image-based user profiles are then constituted by aggregating the points of interest detected for each user. In addition, ground truth visits were determined for the test subset in order to enable accurate evaluation. Finally, we benchmark Vis2Rec using various existing recommender systems, and discuss the possibilities opened up by the availability of user images, as well as the societal issues that come with them. Following good practice in dataset sharing, Vis2Rec is created using only freely distributable content, and additional anonymization is performed to ensure the privacy of users. The raw dataset and the preprocessed user profiles will be publicly available at https://***/MSoumm/Vis2Rec.

关键词： Visualization Data privacy Image databases Semantics multimedia Web sites Benchmark testing Streaming media

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