Can High-Level Concepts Fill the Semantic Gap in Video Retrieval? A Case Study With Broadcast News
Can High-Level Concepts Fill the Semantic Gap in Video Retrieval? A Case Study With Broadcast News
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DOI：
10.1109/tmm.2007.900150
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发表时间：
2007-08
影响因子：
7.3
通讯作者：
Alexander Hauptmann;Rong Yan;Wei-Hao Lin;Michael G. Christel;H. Wactlar
中科院分区：
文献类型：
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作者：
Alexander Hauptmann;Rong Yan;Wei-Hao Lin;Michael G. Christel;H. Wactlar
许多研究人员一直在构建高级语义概念检测器，如户外、人脸、建筑，以帮助语义视频检索。我们的目标是研究需要多少概念，以及如何选择和使用它们。对不同概念检测精度假设下的视频检索性能进行了仿真，结果表明，如果结合足够多的概念，即使在检测精度较低的情况下，也能达到较好的检索效果。我们还得出了关于对大型概念词典最有帮助的概念类型的建议。由于我们的用户研究发现，人们无法预测哪些概念将有助于他们的查询，我们还建议了一些方法来找到最好的概念来使用。最后，本文得出结论：在广播新闻检索中，使用少于5000个概念的基于概念的视频检索，以10%的平均精确度检测，可能会提供高准确率的结果。
A number of researchers have been building high-level semantic concept detectors such as outdoors, face, building, to help with semantic video retrieval. Our goal is to examine how many concepts would be needed, and how they should be selected and used. Simulating performance of video retrieval under different assumptions of concept detection accuracy, we find that good retrieval can be achieved even when detection accuracy is low, if sufficiently many concepts are combined. We also derive suggestions regarding the types of concepts that would be most helpful for a large concept lexicon. Since our user study finds that people cannot predict which concepts will help their query, we also suggest ways to find the best concepts to use. Ultimately, this paper concludes that "concept-based" video retrieval with fewer than 5000 concepts, detected with a minimal accuracy of 10% mean average precision is likely to provide high accuracy results in broadcast news retrieval.