Content-based video indexing of TV broadcast news using hidden Markov models
Content-based video indexing of TV broadcast news using hidden Markov models
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DOI：
10.1109/icassp.1999.757471
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发表时间：
1999-03
期刊：
影响因子：
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通讯作者：
S. Eickeler;Stefan Müller
中科院分区：
文献类型：
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作者：
S. Eickeler;Stefan Müller
本文提出了一种新的方法，基于内容的视频索引，使用隐马尔可夫模型（HALGOT）。在该方法中，针对视频序列的每个图像计算一个特征向量。这些特征向量建模和分类，使用HALTORY。与其他视频索引方法相比，这种方法具有许多优点。该系统具有自动学习能力。它通过呈现手动索引的视频序列来训练。为了改进系统，我们使用了一个视频模型，它允许对复杂的视频序列进行分类。所提出的方法的工作速度比实时快三倍。我们在电视广播新闻上测试了我们的系统。97.3%的正确分类率显示了我们的系统的效率。
This paper presents a new approach to content-based video indexing using hidden Markov models (HMMs). In this approach one feature vector is calculated for each image of the video sequence. These feature vectors are modeled and classified using HMMs. This approach has many advantages compared to other video indexing approaches. The system has automatic learning capabilities. It is trained by presenting manually indexed video sequences. To improve the system we use a video model, that allows the classification of complex video sequences. The presented approach works three times faster than real-time. We tested our system on TV broadcast news. The rate of 97.3% correctly classified frames shows the efficiency of our system.