On the detection of semantic concepts at TRECVID
Proceedings of the 12th annual ACM international conference on Multimedia
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Personalized News Video Recommendation Via Interactive Exploration
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Personalized News Video Recommendation
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
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Most existing web video search engines index videos by file names, URLs, and surrounding texts. These types of video metadata roughly describe the whole video in an abstract level without taking the rich content, such as semantic content descriptions and speech within the video, into consideration. Therefore the relevance ranking of the video search results is not satisfactory as the details of video contents are ignored. In this paper we propose a novel relevance ranking approach for Web-based video search using both video metadata and the rich content contained in the videos. To leverage real content into ranking, the videos are segmented into shots, which are smaller and more semantic-meaningful retrievable units, and then more detailed information of video content such as semantic descriptions and speech of each shots are used to improve the retrieval and ranking performance. With video metadata and content information of shots, we developed an integrated ranking approach, which achieves improved ranking performance. We also introduce machine learning into the ranking system, and compare them with IR-model (information retrieval model) based method. The evaluation results demonstrate the effectiveness of the proposed ranking methods.