The International Arab Journal of Information Technology (IAJIT)


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

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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[19] Zlitni T., Bouaziz B., and Walid M., “Automatic Topics Segmentation for TV news Video Using Prior Knowledge,” Multimedia Tools and Applications, vol. 75, no. 10, pp. 5645- 5672, 2015. 278 The International Arab Journal of Information Technology, Vol. 18, No. 3, May 2021 Mounira Hmayda received the M.S Degree in multimedia computer in 2011 from the University of Gabes, TUNISIA, where she is pursuing the Ph.D. degree in computer science. His research interests focus on video and image processing and analysis, multimedia indexing, and content-based video segmentation and structuring. Ridha Ejbali received the HDR, the Ph.D degree in Computer Engineering, Master degree and computer engineer degree from the National Engineering School of Sfax Tunisia (ENIS) respectively in 2012, 2006 and 2004. He joined the faculty of sciences of Gabes Tunisia (FSG) where he is an assistant in the Department computer sciences since 2012. Since now, he is assistant professor in faculty of sciences of Gabes Tunisia (FSG). His research area is now in pattern recognition and machine learning using Wavelets and Wavelet networks theories. He is IEEE senior Member. Mourad Zaied Professor received the HDR, the Ph.D degrees in Computer Engineering and the Master of Science from the National Engineering School of Sfax respectively in 2013, 2008 and in 2003. He obtained the degree of Computer Engineer from the National Engineering School of Monastir in 1995. Since 1997 he served in several institutes and faculties in university of Gabes as teaching assistant. He joined in 2007 the National Engineering School of Gabes (ENIG) as where he is currently an associate professor in the Department of Electrical Engineering. He is a member of the REsearch Team on Intelligent Machines (RTIM) in the National Engineering School of Gabes (ENIG) since 2001.