Name
Title | ||
|---|---|---|
Localized content-based image retrieval using semi-supervised multiple instance learning |
Abstract | ||
|---|---|---|
In this paper, we propose a Semi-Supervised Multiple-Instance Learning (SSMIL) algorithm, and apply it to Localized Content-Based Image Retrieval (LCBIR), where the goal is to rank all the images in the database, according to the object that users want to retrieve. SSMIL treats LCBIR as a Semi-Supervised Problem and utilize the unlabeled pictures to help improve the retrieval performance. The comparison result of SSMIL with several state-of-art algorithms is promising. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-76386-4_16 | ACCV (1) |
Keywords | Field | DocType |
semi-supervised multiple instance learning,semi-supervised problem,localized content-based image,retrieval performance,semi-supervised multiple-instance learning,unlabeled picture,comparison result,localized content-based image retrieval,state-of-art algorithm | Automatic image annotation,Instance-based learning,Pattern recognition,Information retrieval,Computer science,Image retrieval,Artificial intelligence,Content-based image retrieval,Visual Word | Conference |
Volume | ISSN | ISBN |
4843 | 0302-9743 | 3-540-76385-6 |
Citations | PageRank | References |
5 | 0.48 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dan Zhang | 1 | 461 | 22.17 |
| Zhenwei Shi | 2 | 559 | 63.11 |
| Yangqiu Song | 3 | 2045 | 103.29 |
| Changshui Zhang | 4 | 5506 | 323.40 |