State-of-the-art and future challenges in video scene detection: a survey

دسته بندی: سازمان و پردازش داده ها
ویرایش:  
نویسندگان: Fabro М.D.,  Böszörmenyi L.  
سری:  
 
ناشر:  
سال نشر:  
تعداد صفحات: 49 
زبان: English 
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) 
حجم فایل: 2 مگابایت 

قیمت کتاب (تومان) : 32,000

In Multimedia Systems, Vol. 19, № 5; (2013), pp. 427-454, doi:10.1007/s00530-013-0306-4 by Manfred Del Fabro, Laszlo Böszörmenyi, Multimedia Systems Volume 19, Issue 5 , pp 427-454
Keywords: video segmentation; scene detection; non-sequential video; survey. Topics: multimedia information systems; computer communication; networksoperating systems; data storage representation; data encryption; computer graphics. Industry sectors: electronics; it & software; telecommunications. Abstract
In the last 15 years much effort has been made in the field of segmentation of videos into scenes. We give a comprehensive overview of the published approaches and classify them into seven groups based on three basic classes of low-level features used for the segmentation process: (1) visual-based, (2) audio-based, (3) text-based, (4) audio-visual-based, (5) visual-textual-based, (6) audio-textual-based and (7) hybrid approaches. We try to make video scene detection approaches better assessable and comparable by making a categorization of the evaluation strategies used. This includes size and type of the dataset used as well as the evaluation metrics. Furthermore, in order to let the reader make use of the survey, we list eight possible application scenarios, including an own section for interactive video scene segmentation, and identify those algorithms that can be applied to them. At the end, current challenges for scene segmentation algorithms are discussed. In the appendix the most important characteristics of the algorithms presented in this paper are summarized in table form. Cover: Date 2013-10-01; Print: ISSN 0942-4962; Online: ISSN 1432-1882;Publisher: Springer Berlin Heidelberg. Author Affiliations: Institute of Information Technology (ITEC), Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria Classification of Scene Segmentation Approaches
Scene Segmentation Methods
Rule-Based Methods: 180 degree rule, action matching rule, film tempo rule, shot/reverse shot rule, establishment/breakdown rule
Graph-Based Methods
Stochastic-Based Methods
Hierarchical and Full vs. Partial Decomposition Video Scene Segmentation: State-of-the-Art
Visual-Based Segmentation
Visual-Based Full Segmentation
Visual-Based Partial Segmentation
Visual Graph-Based Full Segmentation
Visual Stochastic-Based Full Segmentation
Audio-Based Segmentation
Audio-Based Full Segmentation
Audio-Based Partial Segmentation
Text-Based Full Segmentation
Audio-Visual Full Segmentation
Audio-Visual Graph-Based Full Segmentation
Audio-Visual Stochastic-Based Full Segmentation
Audio-Visual Stochastic-Based Partial Segmentation
Hybrid Full Segmentation
Visual-Textual Full Segmentation
Audio-Textual Full Segmentation
Hybrid Partial Segmentation
Audio-Textual Partial Segmentation Evaluation of Video Segmentation Approaches
Datasets and Video Genres
Evaluation Methods Strategies for Video Scene Segmentation Problems
Movies
Presented approaches for movies
Presented approaches for movies
TV series or sitcoms
News
Presented approaches for news videos
Possible approaches for news videos
Game and TV show videos
Presented approaches for game and TV show videos
Possible approaches for game and TV show videos
Sports videos
Presented approaches for sports videos
Possible approaches for sports videos
Single-shot videos
Possible approaches for single-shot videos
Black-and-white videos
Presented approaches for black-and-white videos
Possible approaches for black-and-white videos
Interactive scene segmentation
Future Challenges in Video Scene Detection References
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