Modeling, clustering, and segmenting video with mixtures of dynamic textures
Modeling, clustering, and segmenting video with mixtures of dynamic textures
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DOI：
10.1109/tpami.2007.70738
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发表时间：
2008-05-01
影响因子：
23.6
通讯作者：
Vasconcelos, Nuno
中科院分区：
文献类型：
--
作者：
Chan, Antoni B.;Vasconcelos, Nuno
附着纹理是一种时空视频生成模型，它将视频序列表示为一个线性动力学系统的观测值。这项工作研究的动态纹理的混合物，一个统计模型，从一个有限的集合的视觉过程，其中每个是一个动态纹理采样的视频序列的合奏。一个期望最大化（EM）算法推导出学习模型的参数，该模型与线性系统，机器学习，时间序列聚类，控制理论和计算机视觉以前的工作。通过实验，它表明，动态纹理的混合物是一个合适的表示的外观和动态的各种视觉过程，传统上一直具有挑战性的计算机视觉（例如，火，蒸汽，水，车辆和行人交通，等等）。当与运动分割中的现有技术的方法（包括时间纹理方法和传统表示（例如，光流或其他局部运动表示）两者）相比时，动态纹理的混合在此类过程的聚类和分割视频的问题中实现了上级性能。
Adynamic texture is a spatio-temporal generative model for video, which represents video sequences as observations from a linear dynamical system. This work studies the mixture of dynamic textures, a statistical model for an ensemble of video sequences that is sampled from a finite collection of visual processes, each of which is a dynamic texture. An expectation-maximization (EM) algorithm is derived for learning the parameters of the model, and the model is related to previous works in linear systems, machine learning, time-series clustering, control theory, and computer vision. Through experimentation, it is shown that the mixture of dynamic textures is a suitable representation for both the appearance and dynamics of a variety of visual processes that have traditionally been challenging for computer vision (for example, fire, steam, water, vehicle and pedestrian traffic, and so forth). When compared with state-of-the-art methods in motion segmentation, including both temporal texture methods and traditional representations (for example, optical flow or other localized motion representations), the mixture of dynamic textures achieves superior performance in the problems of clustering and segmenting video of such processes.