New approach for dynamic textures discrimination

The aim of this paper is segmenting a sequence of images containing dynamic textures. The proposed method is based on means of features extracted from spatio-temporal cooccurrence matrices that characterize the textures themselves as well as their movements. Features with the highest discriminating power are selected according to a supervised scheme that permits to represent the dynamic textures in a relatively small dimensional space where they can be easily discriminated. Then the selected features are used in the segmentation phase. This approach has been applied in order to automatically identify the red alga (Gelidium Sesquipedale) on sequences of submarine images.

[1]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[2]  P. Bolon,et al.  Analyse d'images: filtrage et segmentation , 1995 .

[3]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[4]  Randal C. Nelson,et al.  Recognition of motion from temporal texture , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Patrick Bouthemy,et al.  Motion characterization from temporal cooccurrences of local motion-based measures for video indexing , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[6]  Alain Fournier,et al.  A simple model of ocean waves , 1986, SIGGRAPH.

[7]  Ming C. Lin,et al.  Feature-Guided Dynamic Texture Synthesis on Continuous Flows , 2007, Rendering Techniques.

[8]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[9]  Zexuan Zhu,et al.  Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Dani Lischinski,et al.  Texture Mixing and Texture Movie Synthesis Using Statistical Learning , 2001, IEEE Trans. Vis. Comput. Graph..

[11]  Daniel Cremers,et al.  Dynamic texture segmentation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[12]  Stefano Soatto,et al.  Dynamic Textures , 2003, International Journal of Computer Vision.

[13]  Martin Szummer,et al.  Temporal texture modeling , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[14]  Ludovic Macaire,et al.  Approach for the discrimination of moving textures , 2009 .

[15]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.

[16]  Robert M. Hawlick.  Statistical and Structural Approaches to Texture , 1979 .