LEARNING TO VIDEO SEARCH RERANK VIA PSEUDO PREFERENCE FEEDBACK

被引：23

作者：

Liu, Yuan ^{[1
]}

Mei, Tao ^{[2
]}

Hua, Xian-Sheng ^{[2
]}

Tang, Jinhui ^{[1
]}

Wu, Xiuqing ^{[1
]}

Li, Shipeng ^{[2
]}

机构：

[1] Univ Sci & Technol China, Hefei 230027, Peoples R China

[2] Microsoft Res Asia, Beijing 100080, Peoples R China

来源：

2008 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-4 | 2008年

关键词：

D O I：

10.1109/ICME.2008.4607430

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：297 / +

页数：2