Name
Abstract | ||
|---|---|---|
Conventional approaches to video search reranking only care whether search results are relevant or irrelevant to the given query, while the ranking order of these results indicating the level of relevance or typicality are usually neglected. This paper presents a novel learning-based approach to video search reranking by investigating the ranking order information. The proposed approach, called pseudo preference feedback (PPF), automatically discovers an optimal set of pseudo preference pairs from the initial ranked list and learns a reranking model by Ranking Support Vector Machines (Ranking SVM) based on the selected pairs. We have proved that PPF can be used for any reranking purpose such as video search and concept detection. We conducted comprehensive experiments for both automatic search and concept detection tasks over TRECVID 2006-2007 benchmark, and showed that PPF could gain significant improvements over the baselines. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICME.2008.4607430 | 2008 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-4 |
Keywords | Field | DocType |
gain,databases,feature extraction,visualization,support vector machines,support vector machine,detectors,kernel | Kernel (linear algebra),Ranking SVM,Ranking,Pattern recognition,Computer science,Visualization,TRECVID,Support vector machine,Feature extraction,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
38 | 1.29 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuan Liu | 1 | 215 | 11.43 |
| Tao Mei | 2 | 4702 | 288.54 |
| Xian-Sheng Hua | 3 | 6566 | 328.17 |
| Jinhui Tang | 4 | 5180 | 212.18 |
| Xiuqing Wu | 5 | 571 | 31.08 |
| Shipeng Li | 6 | 3902 | 252.94 |