Re-ranking by local re-scoring for video indexing and retrieval
Re-ranking by local re-scoring for video indexing and retrieval
复制标题
通过本地重新评分进行重新排名以进行视频索引和检索
DOI：
10.1145/2063576.2063895
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
G. Quénot
中科院分区：
文献类型：
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作者：
Bahjat Safadi;G. Quénot
视频检索可以通过根据分类器预测的样本的概率得分对样本进行排名来完成。通常可以通过重新排序样本来提高检索性能。在本文中，我们提出了一种重新排序的方法，提高了语义视频索引和检索的性能，通过重新评估的同质性和视频的性质，他们属于的镜头的分数。与以前的作品相比，所提出的方法提供了一个框架，通过均匀分布的视频镜头内容的时间序列的重新排名。实验结果表明，在TRECVID 2010语义索引任务中，对于内容同质的视频集合，所提出的重排序方法能够使系统性能平均提高约18%。对于TRECVID 2008，在具有非同质内容的视频集合的情况下，系统性能提高了约11- 13%。
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%.