Paper Info
Title
Ranking Highlight Level of Movie Clips: A Template Based Adaptive Kernel SVM Method.
Abstract
This paper looks into a new direction in movie clips analysis – model based ranking of highlight level. A movie clip, containing a short story, is composed of several continuous shots, which is much simpler than the whole movie. As a result, clip based analysis provides a feasible way for movie analysis and interpretation. In this paper, clip-based ranking of highlight level is proposed, where the challenging problem in detecting and recognizing events within clips is not required. Due to the lack of publicly available datasets, we firstly construct a database of movie clips, where each clip is associated with manually derived highlight level as ground truth. From each clip a number of effective visual cues are then extracted. To bridge the gap between low-level features and highlight level semantics, a holistic method of highlight ranking model is introduced. According to the distance between testing clips and selected templates, appropriate kernel function of support vector machine (SVM) is adaptively selected. Promising results are reported in automatic ranking of movie highlight levels.
Year
DOI
Venue
2015
10.1016/j.jvlc.2014.10.015
Journal of Visual Languages & Computing
Keywords
DocType
Volume
Video analysis,Highlight level,Movie clip,Template based method,Adaptive kernel SVM
Journal
27
ISSN
Citations 
PageRank 
1045-926X
0
0.34
References 
Authors
16
5
Name
Order
Citations
PageRank
Zheng Wang1848.26
Gaojun Ren200.34
Meijun Sun37411.77
Jinchang Ren4114488.54
Jesse S. Jin500.34