Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 909-926 |
Journal / Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Volume | 30 |
Issue number | 5 |
Online published | 5 Jul 2007 |
Publication status | Published - May 2008 |
Externally published | Yes |
Link(s)
Abstract
A dynamic 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 (e.g. fire, steam, water, vehicle and pedestrian traffic, etc.). When compared with state-of-the-art methods in motion segmentation, including both temporal texture methods and traditional representations (e.g. 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.
Research Area(s)
- Dynamic texture, Expectation-maximization, Kalman filter, Linear dynamical systems, Mixture models, Motion segmentation, Probabilistic models, Temporal textures, Time-series clustering, Video clustering, Video modeling
Citation Format(s)
Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures. / Chan, Antoni B.; Vasconcelos, Nuno.
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 5, 05.2008, p. 909-926.
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 5, 05.2008, p. 909-926.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review