Abstract
This paper introduces a novel framework for generating pictorial storylines for given topics from text and image data on the Internet. Unlike traditional text summarization and timeline generation systems, the proposed framework combines text and image analysis and delivers a storyline containing textual, pictorial, and structural information to provide a sketch of the topic evolution. A key idea in the framework is the use of an approximate solution for the dominating set problem. Given a collection of topic-related objects consisting of images and their text descriptions, a weighted multi-view graph is first constructed to capture the contextual and temporal relationships among these objects. Then the objects are selected by solving the minimum-weighted connected dominating set problem defined on this graph. Comprehensive experiments on real-world data sets demonstrate the effectiveness of the proposed framework.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the National Conference on Artificial Intelligence |
| Pages | 683-689 |
| Number of pages | 7 |
| Volume | 1 |
| State | Published - 2012 |
| Event | 26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12 - Toronto, ON, Canada Duration: Jul 22 2012 → Jul 26 2012 |
Other
| Other | 26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12 |
|---|---|
| Country | Canada |
| City | Toronto, ON |
| Period | 7/22/12 → 7/26/12 |
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ASJC Scopus subject areas
- Software
- Artificial Intelligence
Cite this
Generating pictorial storylines via minimum-weight connected dominating set approximation in multi-view graphs. / Wang, Dingding; Li, Tao; Ogihara, Mitsunori.
Proceedings of the National Conference on Artificial Intelligence. Vol. 1 2012. p. 683-689.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Generating pictorial storylines via minimum-weight connected dominating set approximation in multi-view graphs
AU - Wang, Dingding
AU - Li, Tao
AU - Ogihara, Mitsunori
PY - 2012
Y1 - 2012
N2 - This paper introduces a novel framework for generating pictorial storylines for given topics from text and image data on the Internet. Unlike traditional text summarization and timeline generation systems, the proposed framework combines text and image analysis and delivers a storyline containing textual, pictorial, and structural information to provide a sketch of the topic evolution. A key idea in the framework is the use of an approximate solution for the dominating set problem. Given a collection of topic-related objects consisting of images and their text descriptions, a weighted multi-view graph is first constructed to capture the contextual and temporal relationships among these objects. Then the objects are selected by solving the minimum-weighted connected dominating set problem defined on this graph. Comprehensive experiments on real-world data sets demonstrate the effectiveness of the proposed framework.
AB - This paper introduces a novel framework for generating pictorial storylines for given topics from text and image data on the Internet. Unlike traditional text summarization and timeline generation systems, the proposed framework combines text and image analysis and delivers a storyline containing textual, pictorial, and structural information to provide a sketch of the topic evolution. A key idea in the framework is the use of an approximate solution for the dominating set problem. Given a collection of topic-related objects consisting of images and their text descriptions, a weighted multi-view graph is first constructed to capture the contextual and temporal relationships among these objects. Then the objects are selected by solving the minimum-weighted connected dominating set problem defined on this graph. Comprehensive experiments on real-world data sets demonstrate the effectiveness of the proposed framework.
UR - http://www.scopus.com/inward/record.url?scp=84868278518&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84868278518&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9781577355687
VL - 1
SP - 683
EP - 689
BT - Proceedings of the National Conference on Artificial Intelligence
ER -