A reranking approach for context-based concept fusion in video indexing and retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Video search re-ranking via multi-graph propagation
Proceedings of the 15th international conference on Multimedia
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Video retrieval can be done by ranking the samples according to their probability scores that were predicted by classifiers. It is often possible to improve the retrieval performance by re-ranking the samples. In this paper, we proposed a re-ranking method that improves the performance of semantic video indexing and retrieval, by re-evaluating the scores of the shots by the homogeneity and the nature of the video they belong to. Compared to previous works, the proposed method provides a framework for the re-ranking via the homogeneous distribution of video shots content in a temporal sequence. The experimental results showed that the proposed re-ranking method was able to improve the system performance by about 18% in average on the TRECVID 2010 semantic indexing task, videos collection with homogeneous contents. For TRECVID 2008, in the case of collections of videos with non-homogeneous contents, the system performance was improved by about 11-13%.