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An Enhanced Ant Colony Clustering Method for Color Image Segmentation
Objectives: The aim of segmentation process is to divide the image into homogeneous, self-consistent region or objects. The segmentation algorithms try to make systematic uses of some physically measured image features. Methods/Statistical Analysis: In the earlier research work, the researchers covered only multi resolution, quad tree structure, ant colony optimization and Otsu method for segmentation object in gray scale but not for color images. To overcome these issues, the segmentation for color image is focused on Enhanced Ant Colony Clustering (EACC) method. Findings: The proposed EACC method has three main parts. The first part is used to isolate the components of the given color image including RGB pixel values. The second part finds the clustering center with the help of combination of statistical and artificial selection. The last part implements EACC algorithm to segment on color image. In the existing method of ACO, the processing time takes longer time to segment the object. At the same time, while comparing the threshold value on existing method is lower than current proposed method of EACC. Applications/Improvements: In this research work, the proposed method achieves better segmentation in color image for the data sets Oxford Flowers 17, Weizmann Horse and MSRC dataset. Further, this work may be extended to different type of images such as, multiband or multispectral images, satellite images, etc. Finally, the experimental results are shown through Mat Lab R2013a.
Enhanced Ant Colony Clustering, RGB Pixel Values, Segmentation, Self-Consistent Region, Threshold Value.
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