Dynamic texture segmentation based on deterministic partially self-avoiding walks

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• 作者：Wesley Nunes Gon?alves ; Odemir Martinez Bruno
• 关键词：Dynamic texture segmentation ; Deterministic partially self-avoiding walks ; k-Means algorithm
• 刊名：Computer Vision and Image Understanding
• 出版年：2013
• 出版时间：September, 2013
• 年：2013
• 卷：117
• 期：9
• 页码：1163-1174
• 全文大小：4317 K

文摘

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.