Video search reranking via information bottleneck principle
Video search reranking via information bottleneck principle
复制标题
DOI：
10.1145/1180639.1180654
复制
发表时间：
2006-10
期刊：
影响因子：
--
通讯作者：
Winston H. Hsu;L. Kennedy;Shih-Fu Chang
中科院分区：
文献类型：
--
作者：
Winston H. Hsu;L. Kennedy;Shih-Fu Chang
我们提出了一种新的和通用的视频/图像重新排序算法，IB重新排序，发现相关和不相关的镜头从文本结果提供的近似相关性的显着视觉模式，从纯文本搜索的结果重新排序。IB重排序方法基于严格的信息瓶颈（IB）原则，找到最优的图像聚类，使文本搜索结果中图像的高维低层视觉特征与搜索相关性之间保持最大互信息。在TRECVID 2003-2005数据集上对该方法进行的评估显示，在文本搜索基线上有了显着的改进，平均性能的相对提高高达23%。该方法不需要用户提供图像搜索示例，但与其他最先进的基于示例的方法相比具有竞争力。该方法也是高度通用的，并执行复杂的模型，这是高度调整特定类别的查询，如命名的人。我们的实验分析也证实了所提出的重排序方法的工作原理，当有足够的经常性的视觉模式在搜索结果中，经常是在多源新闻视频的情况下。
We propose a novel and generic video/image reranking algorithm, IB reranking, which reorders results from text-only searches by discovering the salient visual patterns of relevant and irrelevant shots from the approximate relevance provided by text results. The IB reranking method, based on a rigorous Information Bottleneck (IB) principle, finds the optimal clustering of images that preserves the maximal mutual information between the search relevance and the high-dimensional low-level visual features of the images in the text search results. Evaluating the approach on the TRECVID 2003-2005 data sets shows significant improvement upon the text search baseline, with relative increases in average performance of up to 23%. The method requires no image search examples from the user, but is competitive with other state-of-the-art example-based approaches. The method is also highly generic and performs comparably with sophisticated models which are highly tuned for specific classes of queries, such as named-persons. Our experimental analysis has also confirmed the proposed reranking method works well when there exist sufficient recurrent visual patterns in the search results, as often the case in multi-source news videos.