NEW TECHNIQUE FOR IMAGE SEGMENTATION: PRESENT WORK

There are many clustering algorithms widely used to solve the segmentation problem (e.g., K-means, FCM, ISODATA and Snob).Here we use new biogeography technique for image segmentation (Taiwi,2004). BBO is a population-based optimization algorithm it does not involve reproduction or the generation of “children.” As we started we select a seed using some set of predefined criteria. After selecting examine neighbor pixels of seed points and calculate MSE color distance between pixels (Auger and Hansen, 2005). If we use RGB image, then we calculate MSE color distance. If we use LAB color space then we calculate CMC color distance between neighboring pixels (Haisong and Hirohisa, 2005). According to the BBO approach make three islands HIS, MSI and LSI.HSI (highly suitability index) that contain pixels which have more similar properties. Medium suitability index (MSI) basically contains pixels which have medially suitable. Low suitability index (LSI) that contain pixels which contain pixels that not so familiar. HSI tend to have a large number of species, while those LSI have a small number of species. HSI have many species that emigrate to nearby habitats, simply by virtue of the large number of species that they host. HSI have a low species immigration rate because they are already nearly saturated with species. Therefore, HSI habitats are more static in their species distribution than LSI habitats. LSI have a high species immigration rate because of their sparse populations. Then we select threshold value (Fritz, Rinck and Dillmann,2006) . If our calculated distance less than threshold then its migrate to other region, otherwise its make its own region.

The Image Segmentation using BBO algorithm can be informally described with the following algorithm.

Step 1) Take an image and convert it into Lab image.
Step 2) Calculate CMC distance between neighboring pixels.
Step 3) Initialize the BBO parameters. Allocate the maximum species count Smax and the maximum migration rates E and I, the maximum mutation rate mmax. The maximum species count and the maximum migration rates are relative quantities.
Step 4) Initialize a random set of habitats, each habitat corresponding to a potential solution to the given problem. For each habitat, map the HSI to the number of species S, the immigration rate , X and mutation ^. Compute S, X,p,, for each solution.
Step 5) Modify habitats (migration) based on X, ^ based on probability.
Step 6) Go to step (3) for the next iteration. This loop can be terminated after a predefined number of generations, or after an acceptable problem solution has been found.

Representative APR 391%

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