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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Layered Dynamic Textures
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Layered Dynamic Textures

机译：分层动态纹理

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摘要

A novel video representation, the layered dynamic texture (LDT), is proposed. The LDT is a generative model, which represents a video as a collection of stochastic layers of different appearance and dynamics. Each layer is modeled as a temporal texture sampled from a different linear dynamical system. The LDT model includes these systems, a collection of hidden layer assignment variables (which control the assignment of pixels to layers), and a Markov random field prior on these variables (which encourages smooth segmentations). An EM algorithm is derived for maximum-likelihood estimation of the model parameters from a training video. It is shown that exact inference is intractable, a problem which is addressed by the introduction of two approximate inference procedures: a Gibbs sampler and a computationally efficient variational approximation. The trade-off between the quality of the two approximations and their complexity is studied experimentally. The ability of the LDT to segment videos into layers of coherent appearance and dynamics is also evaluated, on both synthetic and natural videos. These experiments show that the model possesses an ability to group regions of globally homogeneous, but locally heterogeneous, stochastic dynamics currently unparalleled in the literature.
机译：提出了一种新颖的视频表示，即分层动态纹理（LDT）。 LDT是一种生成模型，它将视频表示为具有不同外观和动态特性的随机层的集合。将每个层建模为从不同的线性动力学系统采样的时间纹理。 LDT模型包括这些系统，隐藏的图层分配变量（控制像素到图层的分配）的集合以及这些变量之前的马尔可夫随机场（鼓励平滑分割）。从训练视频中导出用于算法参数最大似然估计的EM算法。结果表明，精确推理是棘手的，通过引入两个近似推理过程可以解决此问题：吉布斯采样器和计算有效的变分近似。实验研究了两个近似值的质量及其复杂性之间的权衡。在合成和自然视频上，还评估了LDT将视频分割为连贯的外观和动态层的能力。这些实验表明，该模型具有对全局均质但局部异质，随机动力学区域进行分组的能力，这在文献中是前所未有的。

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