A Novel Video Summarization Based on Mining the Story-Structure and Semantic Relations Among Concept Entities

Video summarization techniques have been proposed for years to offer people comprehensive understanding of the whole story in the video. Roughly speaking, existing approaches can be classified into the two types: one is static storyboard, and the other is dynamic skimming. However, despite that thes...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on multimedia 2009-02, Vol.11 (2), p.295-312
Main Authors: Chen, Bo-Wei, Wang, Jia-Ching, Wang, Jhing-Fa
Format: Article
Language:English
Subjects:
Browsing
Concatenated codes
Concept expansion tree
Construction
Councils
Displays
Dynamical systems
Dynamics
Entropy
Feature extraction
graph entropy
graph mining
Graphs
Gunshot detection systems
Indexing
Mathematical models
Semantics
Speech
structural video contents
Tree graphs
video browsing
video indexing
video summarization
Visualization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Video summarization techniques have been proposed for years to offer people comprehensive understanding of the whole story in the video. Roughly speaking, existing approaches can be classified into the two types: one is static storyboard, and the other is dynamic skimming. However, despite that these traditional methods give brief summaries for users, they still do not provide with a concept-organized and systematic view. In this paper, we present a structural video content browsing system and a novel summarization method by utilizing the four kinds of entities: who, what, where, and when to establish the framework of the video contents. With the assistance of the above-mentioned indexed information, the structure of the story can be built up according to the characters, the things, the places, and the time. Therefore, users can not only browse the video efficiently but also focus on what they are interested in via the browsing interface. In order to construct the fundamental system, we employ maximum entropy criterion to integrate visual and text features extracted from video frames and speech transcripts, generating high-level concept entities. A novel concept expansion method is introduced to explore the associations among these entities. After constructing the relational graph, we exploit graph entropy model to detect meaningful shots and relations, which serve as the indices for users. The results demonstrate that our system can achieve better performance and information coverage.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2008.2009703