Effectiveness of deep learning techniques in TV programs classification: A comparative analysis

IF 5.8 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Integrated Computer-Aided Engineering Pub Date : 2024-04-10 DOI:10.3233/ica-240740
Federico Candela, Angelo Giordano, Carmen Francesca Zagaria, Francesco Carlo Morabito
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Abstract

In the application areas of streaming, social networks, and video-sharing platforms such as YouTube and Facebook, along with traditional television systems, programs’ classification stands as a pivotal effort in multimedia content management. Despite recent advancements, it remains a scientific challenge for researchers. This paper proposes a novel approach for television monitoring systems and the classification of extended video content. In particular, it presents two distinct techniques for program classification. The first one leverages a framework integrating Structural Similarity Index Measurement and Convolutional Neural Network, which pipelines on stacked frames to classify program initiation, conclusion, and contents. Noteworthy, this versatile method can be seamlessly adapted across various systems. The second analyzed framework implies directly processing optical flow. Building upon a shot-boundary detection technique, it incorporates background subtraction to adaptively discern frame alterations. These alterations are subsequently categorized through the integration of a Transformers network, showcasing a potential advancement in program classification methodology. A comprehensive overview of the promising experimental results yielded by the two techniques is reported. The first technique achieved an accuracy of 95%, while the second one surpassed it with an even higher accuracy of 87% on multiclass classification. These results underscore the effectiveness and reliability of the proposed frameworks, and pave the way for a more efficient and precise content management in the ever-evolving landscape of multimedia platforms and streaming services.

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深度学习技术在电视节目分类中的有效性:对比分析
摘要 在流媒体、社交网络、YouTube 和 Facebook 等视频共享平台以及传统电视系统等应用领域,节目分类是多媒体内容管理的关键工作。尽管近年来取得了一些进展,但它仍然是研究人员面临的一项科学挑战。本文为电视监控系统和扩展视频内容分类提出了一种新方法。特别是,它提出了两种不同的节目分类技术。第一种技术利用结构相似性指数测量和卷积神经网络的整合框架,通过对堆叠帧进行流水线处理来对节目的开始、结束和内容进行分类。值得注意的是,这种通用方法可无缝适用于各种系统。第二个分析框架是直接处理光流。它以镜头边界检测技术为基础,结合背景减影技术,自适应地识别帧的变化。随后,通过整合变形金刚网络对这些变化进行分类,展示了程序分类方法的潜在进步。报告全面概述了这两种技术所取得的令人鼓舞的实验结果。第一种技术的准确率达到 95%,而第二种技术在多类分类中的准确率更高达 87%,超过了第一种技术。这些结果凸显了建议框架的有效性和可靠性,为在不断发展的多媒体平台和流媒体服务中实现更高效、更精确的内容管理铺平了道路。
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来源期刊
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering 工程技术-工程:综合
CiteScore
9.90
自引率
21.50%
发文量
21
审稿时长
>12 weeks
期刊介绍: Integrated Computer-Aided Engineering (ICAE) was founded in 1993. "Based on the premise that interdisciplinary thinking and synergistic collaboration of disciplines can solve complex problems, open new frontiers, and lead to true innovations and breakthroughs, the cornerstone of industrial competitiveness and advancement of the society" as noted in the inaugural issue of the journal. The focus of ICAE is the integration of leading edge and emerging computer and information technologies for innovative solution of engineering problems. The journal fosters interdisciplinary research and presents a unique forum for innovative computer-aided engineering. It also publishes novel industrial applications of CAE, thus helping to bring new computational paradigms from research labs and classrooms to reality. Areas covered by the journal include (but are not limited to) artificial intelligence, advanced signal processing, biologically inspired computing, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, intelligent and adaptive systems, internet-based technologies, knowledge discovery and engineering, machine learning, mechatronics, mobile computing, multimedia technologies, networking, neural network computing, object-oriented systems, optimization and search, parallel processing, robotics virtual reality, and visualization techniques.
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