Dynamic texture segmentation based on deterministic partially self-avoiding walks |
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| Authors: | Wesley Nunes Gonçalves Odemir Martinez Bruno |
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| Affiliation: | 1. Dept. of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, United States;2. MacNeal Hospital, Berwyn, IL 60402, United States;3. New Jersey Institute of Radiology, Carlstadt, NJ 07072, United States;4. Dept. of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44120, United States;1. Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089, USA;2. Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA;3. David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA;4. Computer Science Department, Chung-Ang University, Seoul, South Korea;1. Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, 710119 Shaanxi, PR China;2. National Institutes of Health, National Cancer Institute, Bethesda, MD 20892, USA;1. Department of Electrical and Computer Engineering, The University of Michigan, Ann Arbor, MI 48105, United States;2. Founder/Chief Scientist at Industrial Perception Inc., CA, United States;1. Cerema – DTer Est – ERA 31, 71 rue de la Grande Haie, F-54510 Tomblaine, France;2. Cerema – DTer Ouest – ERA 17, 23 avenue de l’Amiral Chauvin, F-49136 Les Ponts de Cé, France;3. LUNAM Université, IFSTTAR – CoSys, SII, route de Bouaye, F-44344 Bouguenais, France, and INRIA/IRISA, I4S Team, Campus de Beaulieu, F-35042 Rennes, France;4. Cerema – DTer Centre Est, 8-10 rue Bernard Palissy, F-63017 Clermont-Ferrand Cedex 2, France;1. Department of Computer Science, UC, Davis One Shields Avenue, Davis, CA 95616, USA;2. Department of Neurology, UC, Davis One Shields Avenue, Davis, CA 95616, USA |
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| Abstract: | 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. |
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| Keywords: | Dynamic texture segmentation Deterministic partially self-avoiding walks |
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