Multiple style exploration for story unit segmentation of broadcast news video

Feng, Bailan; Chen, Zhineng; Zheng, Rong; Xu, Bo

doi:10.1007/s00530-013-0350-0

Cited by 10 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second group of program and story segmentation approaches rely on presentation styles used in the production of TV news. Presentation styles are characterized using a variety of features, such as anchor shots (Feng et al, 2014;Zlitni et al, 2015) video jingles (Zlitni et al, 2015;Feng et al, 2012) occurrence of the face in a special area of screen (Qu et al, 2004;Poulisse el al., 1 2 5 6 7 3 4 10 11 8 9 12 13 14 19 16 15 18 17 20 Distance Face number 2010), audio events (Browne et al, 2002;Meinedo el al., 2003), presence or absence of text at particular locations (Jindal et al, 2011;Ghosh et al, 2010), etc. Video segmentation is the first important step in video content analysis. It aims at dividing the video stream into a set of meaningful and manageable segments (shots) that are used as basic elements for indexing (Weiming et al, 2011).…”

Section: Related Workmentioning

confidence: 99%

Classification Program and Story Boundaries Segmentation in TV News Broadcast Videos via Deep Convolutional Neural Network

Hmayda¹,

Ejbali²,

Zaied³

2020

Journal of Computer Science

View full text Add to dashboard Cite

Given the amount of video information on the net, the user has had difficulty finding the information in a reasonable amount of time. Thus, all video content must be segmented and annotated so that he/she can access the information directly. The goal of the proposed approach is to allow a better exploitation of video by multimedia services (TV-On-Demand, catch-up TV), social community and video-sharing platforms (Youtube, Facebook…). In this work, an approach to classify TV programs and story boundaries segmentation in TV news broadcast video using Deep Convolutional Neural Network (DCNN) is presented. The first step is to extract features from video. This characteristics will modeled as video corpus governings the organization of TV stream content. This organization is carried out on two levels. The first consists in the identification of anchorperson by Single-Linkage Clustering through CNN faces and the second level aims to identify the story of news program due to the large audience because of the pertinent information they contain. In addition, we implement a 360-h broadcast video dataset obtained from five French news channels with ground-truth marked semantic shot categories, program genres and story boundaries. Experiments on this dataset prove the relevance of our approach for news broadcast video segmentation.

show abstract