Learning to video search rerank via pseudo preference feedback
Yuan Liu
Tao Mei
Xian-Sheng Hua
Jinhui Tang
Xiuqing Wu
Shipeng Li
Univ. of Sci. & Technol. of China, Hefei;
This paper appears in: Multimedia and Expo, 2008 IEEE International Conference on
Publication Date: June 23 2008-April 26 2008
On page(s): 297-300
Location: Hannover,
ISBN: 978-1-4244-2570-9
INSPEC Accession Number: 10178853
Digital Object Identifier: 10.1109/ICME.2008.4607430
Current Version Published: 2008-08-26
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.
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