Automatic Topics Segmentation for News Video by Clustering of Histogram of Orientation Gradients Faces

TV stream is a major source of multimedia data. The proposed method aims to enable a good exploitation of this source of video by multimedia services social community, and video-sharing platforms In this work, we propose an approach to the automatic topics segmentation of news video. The originality of the approach is the use of Clustering of Histogram of Orientation Gradients (HOG) faces as prior knowledge. This knowledge is modeled as images which governs the structuring of TV stream content. This structuring is carried out on two levels. The first consists in the identification of anchorperson by Single-Linkage Clustering of HOG faces. The second level aims to identify the topics of news program due to the large audience because of the pertinent information they contain. Experiments comparing the proposed technique to similar works were carried out on the TREC Video Retrieval Evaluation (TRECVID) 2003 database. The results show significant improvements to TV news structuring exceeding 96 %.

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Extraction and Annotation of News Topics From TV Streams for Web Video Sharing

Knowledge-Intensive Economies and Opportunities for Social, Organizational, and Technological Growth - Advances in Knowledge Acquisition, Transfer, and Management ◽

10.4018/978-1-5225-7347-0.ch014 ◽

2019 ◽

pp. 272-294

Author(s):

Tarek Zlitni ◽

Walid Mahdi

Keyword(s):

Video On Demand ◽

Added Value ◽

Automatic Process ◽

Video Sharing ◽

News Program ◽

Operational Characteristics ◽

Web Video ◽

Tv News ◽

Tv Program ◽

Program Content

Today, with increased internet access, users are often interested in new content-based multimedia applications of high added value such as interactive TV, video on demand (VoD), and catch-up TV services such as YouTube or Dailymotion frameworks. Despite the easy and rapid access to media information of these services, they present the risk of the wide propagation of fake news. As a solution, the authors propose that the input for these services must be from a trustworthy traditional media, precisely TV program content. So, the automatic process of TV program identification and their internal segmentation facilitate the availability of these programs. In this chapter, the major originality of the authors' approach is the use of contextual and operational characteristics of TV production rules as prior knowledge that captures the structure for recurrent TV news program content. The authors validate their approach by experiments conducted using the TRECVID dataset that demonstrate its robustness.

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A Distributed Content-Based Video Retrieval System for Large Data-sets

10.21203/rs.3.rs-255106/v1 ◽

2021 ◽

Author(s):

ElMehdi SAOUDI ◽

Said Jai Andaloussi

Keyword(s):

Real Time ◽

Retrieval System ◽

Video Retrieval ◽

Multimedia Databases ◽

Video Data ◽

Multimedia Data ◽

Machine Learning Techniques ◽

Data Sets ◽

Data Set ◽

Content Based Video Retrieval

Abstract With the rapid growth of the volume of video data and the development of multimedia technologies, it has become necessary to have the ability to accurately and quickly browse and search through information stored in large multimedia databases. For this purpose, content-based video retrieval ( CBVR ) has become an active area of research over the last decade. In this paper, We propose a content-based video retrieval system providing similar videos from a large multimedia data-set based on a query video. The approach uses vector motion-based signatures to describe the visual content and uses machine learning techniques to extract key-frames for rapid browsing and efficient video indexing. We have implemented the proposed approach on both, single machine and real-time distributed cluster to evaluate the real-time performance aspect, especially when the number and size of videos are large. Experiments are performed using various benchmark action and activity recognition data-sets and the results reveal the effectiveness of the proposed method in both accuracy and processing time compared to state-of-the-art methods.

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Political Information Use and Its Relationship to Beliefs in Conspiracy Theories Among the German Public

Journalism & Mass Communication Quarterly ◽

10.1177/10776990211063527 ◽

2021 ◽

pp. 107769902110635

Author(s):

Christian Schemer ◽

Marc Ziegele ◽

Tanjev Schultz ◽

Oliver Quiring ◽

Nikolaus Jackob ◽

...

Keyword(s):

Public Service ◽

Cross Sectional ◽

Conspiracy Theories ◽

Video Sharing ◽

News Sources ◽

User Comments ◽

Tv News ◽

Party Preference ◽

Left And Right ◽

Frequent Exposure

This study investigates how exposure to different news sources, propensity to vote (PTV) for a party and demographics are related to belief in conspiracy theories drawing on three repeated cross-sectional surveys in Germany 2017–2019. Results show that frequent exposure to alternative news sites and video-sharing platforms increased conspiratorial beliefs. Frequency of exposure to the quality press, public service TV news, and news aggregators diminished beliefs in conspiracy theories. Exposure to TV news, legacy media online, tabloids, social media, and user comments was unrelated to such beliefs. PTV for far left and right parties increased conspiratorial beliefs, moderate party preference reduced them.

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Semantic Analysis of Videos for Tags Prediction and Segmentation

Industrial Internet of Things and Cyber-Physical Systems - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-7998-2803-7.ch014 ◽

2020 ◽

pp. 296-307

Author(s):

Umair Ali Khan

Keyword(s):

Semantic Analysis ◽

Video Segmentation ◽

Video Retrieval ◽

Relevant Literature ◽

Multimedia Data ◽

Compact Set ◽

Incident Detection ◽

Research Areas ◽

Pros And Cons ◽

Manual Methods

Due to the growing volume of multimedia data generated these days, it has become extremely difficult to manually analyze the data and extract useful information from it. Especially the analysis of videos pertaining to different fields such as surveillance, videos, social media, education, etc. cannot be done efficiently by manual methods. This requires automatic analysis algorithms that can intelligently analyze videos and derive salient information from them. This information can be useful in a number of tasks such as video segmentation, incident detection, anomaly detection, query-based video retrieval, and content censorship. This chapter provides a detailed review of the techniques proposed for video analysis to provide a compact set of video tags. This chapter considers it a joint tag-segmentation problem and critically analyzes the relevant literature to highlight their respective pros and cons. At the end, potential research areas in this domain and suggestions for improvement are discussed.

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