Name
Title | ||
|---|---|---|
Pseudo relevance feedback based on iterative probabilistic one-class SVMs in web image retrieval |
Abstract | ||
|---|---|---|
To improve the precision of top-ranked images returned by a web image search engine, we propose in this paper a novel pseudo relevance feedback method named iterative probabilistic one-class SVMs to re-rank the retrieved images. By assuming that most top-ranked images are relevant to the query, we iteratively train one-class SVMs, and convert the outputs to probabilities so as to combine the decision from different image representation. The effectiveness of our method is validated by systematic experiments even if the assumption is not well satisfied. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-30542-2_27 | PCM (2) |
Keywords | Field | DocType |
one-class svms,iterative probabilistic one-class svms,novel pseudo relevance feedback,web image search engine,top-ranked image,web image retrieval,systematic experiment,different image representation,satisfiability,image retrieval | Search engine,Relevance feedback,Pattern recognition,Computer science,Support vector machine,Image representation,Image retrieval,Artificial intelligence,Probabilistic logic,Web image,Machine learning | Conference |
Volume | ISSN | ISBN |
3332 | 0302-9743 | 3-540-23977-4 |
Citations | PageRank | References |
8 | 0.66 | 13 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingrui He | 1 | 977 | 75.40 |
| Mingjing Li | 2 | 3076 | 192.39 |
| Zhiwei Li | 3 | 1315 | 107.73 |
| Hong-Jiang ZHANG | 4 | 17378 | 1393.22 |
| Hanghang Tong | 5 | 3560 | 202.37 |
| Changshui Zhang | 6 | 5506 | 323.40 |