Unsupervised texture segmentation using multiresolution hybrid genetic algorithm
UNSPECIFIED (2003) Unsupervised texture segmentation using multiresolution hybrid genetic algorithm. In: IEEE International Conference on Image Processing, SEP 14-17, 2003, BARCELONA, SPAIN.Full text not available from this repository.
This work approaches the texture segmentation problem by incorporating genetic algorithm and k-mean clustering method within a multiresolution structure. First, a quad-tree structure is constructed and the input image is partition into blocks at different resolution levels. Texture features are then extracted from each block. Based on the texture features, a hybrid genetic algorithm is employed to perform the segmentation. The crossover operator of traditional genetic algorithm is replaced with k-means clustering method while the mutate and select operators are adopted. In the final step, the boundaries and the segmentation result of the current resolution level are propagated down to the next level to act as contextual constraints and the initial configuration of the next level, respectively.
|Item Type:||Conference Item (UNSPECIFIED)|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TR Photography
|Journal or Publication Title:||2003 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL 2, PROCEEDINGS|
|Number of Pages:||4|
|Page Range:||pp. 1033-1036|
|Title of Event:||IEEE International Conference on Image Processing|
|Location of Event:||BARCELONA, SPAIN|
|Date(s) of Event:||SEP 14-17, 2003|
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