Adaptive Subspace Symbolization for Content-Based Video Detection
Adaptive Subspace Symbolization for Content-Based Video Detection
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DOI：
10.1109/tkde.2009.171
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发表时间：
2010-10
影响因子：
8.9
通讯作者：
Xiangmin Zhou;Xiaofang Zhou;Lei Chen;Y. Shu;A. Bouguettaya;John A. Taylor
中科院分区：
文献类型：
--
作者：
Xiangmin Zhou;Xiaofang Zhou;Lei Chen;Y. Shu;A. Bouguettaya;John A. Taylor
高效、有效地识别相似视频是基于内容的视频检索中的一个重要问题。针对海量视频数据库的内容检索问题，提出了一种子空间符号化方法--SUDS。SODS的新奇之处在于，它探索子空间中的数据分布，通过推导字符串匹配技术和两步数据简化来构建视频处理的视觉词典。具体地说，我们首先提出了一种自适应的方法，称为VLP，从整个视觉特征空间中提取一系列长度可变的优势子空间，而不受维度连续性的限制。通过在每个主导子空间上对视频关键帧进行聚类来构建稳定的视觉词典。紧凑的视频表示模型是通过将每个关键帧转换为一个单词，该单词是占主导地位的子空间中的一系列符号，并进一步将每个视频转换为一系列单词。在此基础上，提出了一种新的相似性度量方法CVE，该方法根据视频的视觉特征和序列上下文信息进行互补信息补偿。最后，提出了一种高效的两层索引策略，并进行了一系列查询优化，以便于视频检索。实验结果证明了该算法的有效性和高效性。
Efficiently and effectively identifying similar videos is an important and nontrivial problem in content-based video retrieval. This paper proposes a subspace symbolization approach, namely SUDS, for content-based retrieval on very large video databases. The novelty of SUDS is that it explores the data distribution in subspaces to build a visual dictionary with which the videos are processed by deriving the string matching techniques with two-step data simplification. Specifically, we first propose an adaptive approach, called VLP, to extract a series of dominant subspaces of variable lengths from the whole visual feature space without the constraint of dimension consecutiveness. A stable visual dictionary is built by clustering the video keyframes over each dominant subspace. A compact video representation model is developed by transforming each keyframe into a word that is a series of symbols in the dominant subspaces, and further each video into a series of words. Then, we present an innovative similarity measure called CVE, which adopts a complementary information compensation scheme based on the visual features and sequence context of videos. Finally, an efficient two-layered index strategy with a number of query optimizations is proposed to facilitate video retrieval. The experimental results demonstrate the high effectiveness and efficiency of SUDS.