Publication Type
Journal Article
Version
publishedVersion
Publication Date
11-2011
Abstract
The explosive growth of Web videos brings out the challenge of how to efficiently browse hundreds or even thousands of videos at a glance. Given an event-driven query, social media Web sites usually return a large number of videos that are diverse and noisy in a ranking list. Exploring such results will be time-consuming and thus degrades user experience. This article presents a novel scheme that is able to summarize the content of video search results by mining and threading "key" shots, such that users can get an overview of main content of these videos at a glance. The proposed framework mainly comprises four stages. First, given an event query, a set of Web videos is collected associated with their ranking order and tags. Second, key-shots are established and ranked based on near-duplicate keyframe detection and they are threaded in a chronological order. Third, we analyze the tags associated with key-shots. Irrelevant tags are filtered out via a representativeness and descriptiveness analysis, whereas the remaining tags are propagated among key-shots by random walk. Finally, summarization is formulated as an optimization framework that compromises relevance of key-shots and user-defined skimming ratio. We provide two types of summarization: video skimming and visual-textual storyboard. We conduct user studies on twenty event queries for over hundred hours of videos crawled from YouTube. The evaluation demonstrates the feasibility and effectiveness of the proposed solution.
Keywords
Algorithm, Design, Experimentation, Event evolution, key-shot threading, key-shot tagging, Web video summarization
Discipline
Graphics and Human Computer Interfaces | Theory and Algorithms
Research Areas
Intelligent Systems and Optimization
Publication
ACM Transactions on Multimedia Computing, Communications and Applications
Volume
7
Issue
4
First Page
1
Last Page
21
ISSN
1551-6857
Identifier
10.1145/2043612.2043613
Publisher
Association for Computing Machinery (ACM)
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.