Segmenting Foreground Objects from a Dynamic Textured Background via a Robust Kalman Filter
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Deterministic Method for Initializing K-Means Clustering
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Optical Flow Estimation and Segmentation of Multiple Moving Dynamic Textures
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Registration Using Dynamic Textures
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Dynamic Texture Detection Based on Motion Analysis
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture analysis and classification using deterministic tourist walk
Pattern Recognition
Modeling music as a dynamic texture
IEEE Transactions on Audio, Speech, and Language Processing
DynTex: A comprehensive database of dynamic textures
Pattern Recognition Letters
Texture analysis based on maximum contrast walker
Pattern Recognition Letters
Segmenting dynamic textures with ising descriptors, ARX models and level sets
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
Dynamic texture analysis and classification using deterministic partially self-avoiding walks
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
IEEE Transactions on Signal Processing
Texture descriptor based on partially self-avoiding deterministic walker on networks
Expert Systems with Applications: An International Journal
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Recently there has been a considerable interest in dynamic textures due to the explosive growth of multimedia databases. In addition, dynamic texture appears in a wide range of videos, which makes it very important in applications concerning to model physical phenomena. Thus, dynamic textures have emerged as a new field of investigation that extends the static or spatial textures to the spatio-temporal domain. In this paper, we propose a novel approach for dynamic texture segmentation based on automata theory and k-means algorithm. In this approach, a feature vector is extracted for each pixel by applying deterministic partially self-avoiding walks on three orthogonal planes of the video. Then, these feature vectors are clustered by the well-known k-means algorithm. Although the k-means algorithm has shown interesting results, it only ensures its convergence to a local minimum, which affects the final result of segmentation. In order to overcome this drawback, we compare six methods of initialization of the k-means. The experimental results have demonstrated the effectiveness of our proposed approach compared to the state-of-the-art segmentation methods.