Abstract
In recent years, we have witnessed a deluge of multimedia data such as texts, images, and videos. However, the research of managing and retrieving these data efficiently is still in the development stage. The conventional tag-based searching approaches suffer from noisy or incomplete tag issues. As a result, the content-based multimedia data management framework has become increasingly popular. In this research direction, multimedia high-level semantic concept mining and retrieval is one of the fastest developing research topics requesting joint efforts from researchers in both data mining and multimedia domains. To solve this problem, one great challenge is to bridge the semantic gap which is the gap between high-level concepts and low-level features. Recently, positive inter-concept correlations have been utilized to capture the context of a concept to bridge the gap. However, negative correlations have rarely been studied because of the difficulty to mine and utilize them. In this paper, a concept mining and retrieval framework utilizing negative inter-concept correlations is proposed. Several research problems such as negative correlation selection, weight estimation, and score integration are addressed. Experimental results on TRECVID 2010 benchmark data set demonstrate that the proposed framework gives promising performance.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2014 IEEE International Conference on Semantic Computing, ICSC 2014 |
| Publisher | IEEE Computer Society |
| Pages | 28-35 |
| Number of pages | 8 |
| ISBN (Print) | 9781479940028 |
| DOIs | |
| State | Published - Jan 1 2014 |
| Event | 8th IEEE International Conference on Semantic Computing, ICSC 2014 - Newport Beach, CA, United States Duration: Jun 16 2014 → Jun 18 2014 |
Other
| Other | 8th IEEE International Conference on Semantic Computing, ICSC 2014 |
|---|---|
| Country | United States |
| City | Newport Beach, CA |
| Period | 6/16/14 → 6/18/14 |
Fingerprint
Keywords
- Information Integration
- Multimedia Semantic Mining and Retrieval
- Negative Correlations
ASJC Scopus subject areas
- Software
Cite this
Enhancing multimedia semantic concept mining and retrieval by incorporating negative correlations. / Meng, Tao; Liu, Yang; Shyu, Mei-Ling; Yan, Yilin; Shu, Chi Min.
Proceedings - 2014 IEEE International Conference on Semantic Computing, ICSC 2014. IEEE Computer Society, 2014. p. 28-35 6881998.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Enhancing multimedia semantic concept mining and retrieval by incorporating negative correlations
AU - Meng, Tao
AU - Liu, Yang
AU - Shyu, Mei-Ling
AU - Yan, Yilin
AU - Shu, Chi Min
PY - 2014/1/1
Y1 - 2014/1/1
N2 - In recent years, we have witnessed a deluge of multimedia data such as texts, images, and videos. However, the research of managing and retrieving these data efficiently is still in the development stage. The conventional tag-based searching approaches suffer from noisy or incomplete tag issues. As a result, the content-based multimedia data management framework has become increasingly popular. In this research direction, multimedia high-level semantic concept mining and retrieval is one of the fastest developing research topics requesting joint efforts from researchers in both data mining and multimedia domains. To solve this problem, one great challenge is to bridge the semantic gap which is the gap between high-level concepts and low-level features. Recently, positive inter-concept correlations have been utilized to capture the context of a concept to bridge the gap. However, negative correlations have rarely been studied because of the difficulty to mine and utilize them. In this paper, a concept mining and retrieval framework utilizing negative inter-concept correlations is proposed. Several research problems such as negative correlation selection, weight estimation, and score integration are addressed. Experimental results on TRECVID 2010 benchmark data set demonstrate that the proposed framework gives promising performance.
AB - In recent years, we have witnessed a deluge of multimedia data such as texts, images, and videos. However, the research of managing and retrieving these data efficiently is still in the development stage. The conventional tag-based searching approaches suffer from noisy or incomplete tag issues. As a result, the content-based multimedia data management framework has become increasingly popular. In this research direction, multimedia high-level semantic concept mining and retrieval is one of the fastest developing research topics requesting joint efforts from researchers in both data mining and multimedia domains. To solve this problem, one great challenge is to bridge the semantic gap which is the gap between high-level concepts and low-level features. Recently, positive inter-concept correlations have been utilized to capture the context of a concept to bridge the gap. However, negative correlations have rarely been studied because of the difficulty to mine and utilize them. In this paper, a concept mining and retrieval framework utilizing negative inter-concept correlations is proposed. Several research problems such as negative correlation selection, weight estimation, and score integration are addressed. Experimental results on TRECVID 2010 benchmark data set demonstrate that the proposed framework gives promising performance.
KW - Information Integration
KW - Multimedia Semantic Mining and Retrieval
KW - Negative Correlations
UR - http://www.scopus.com/inward/record.url?scp=84906978998&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84906978998&partnerID=8YFLogxK
U2 - 10.1109/ICSC.2014.30
DO - 10.1109/ICSC.2014.30
M3 - Conference contribution
SN - 9781479940028
SP - 28
EP - 35
BT - Proceedings - 2014 IEEE International Conference on Semantic Computing, ICSC 2014
PB - IEEE Computer Society
ER -