Abstract
Concept-based multimedia search has become more and more popular in Multimedia Information Retrieval (MIR). However, which semantic concepts should be used for data collection and model construction is still an open question. Currently, there is very little research found on automatically choosing multimedia concepts with small semantic gaps. In this paper, we propose a novel framework to develop a lexicon of high-level concepts with small semantic gaps (LCSS) from a large-scale web image dataset. By defining a confidence map and content-context similarity matrix, images with small semantic gaps are selected and clustered. The final concept lexicon is mined from the surrounding descriptions (titles, categories and comments) of these images. This lexicon offers a set of high-level concepts with small semantic gaps, which is very helpful for people to focus for data collection, annotation and modeling. It also shows a promising application potential for image annotation refinement and rejection. The experimental results demonstrate the validity of the developed concepts lexicon. ©2008 IEEE.
| Original language | English |
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| Title of host publication | 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR |
| DOIs | |
| Publication status | Published - 2008 |
| Externally published | Yes |
| Event | 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR - Anchorage, AK, United States Duration: 23 Jun 2008 → 28 Jun 2008 |
Publication series
| Name | 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR |
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Conference
| Conference | 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR |
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| Place | United States |
| City | Anchorage, AK |
| Period | 23/06/08 → 28/06/08 |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Funding
This work is supported in Part by ARO Grant W911NF05-1-0404, and by DHS Grant N0014-07-1-0151. We also thank Dr. Changhu Wang for valuable discussions.