Classifying Color Codes Via k-Mean Clustering and L*a*b* Color Model

Image Segmentation Via Color Clustering

This paper develops a computationally efficient process for segmentation of color images. The input image is partitioned into a set of output images in accordance to color characteristics of various image regions. The algorithm is based on random sampling of the input image and fuzzy clustering of the training data followed by crisp classification of the input image. The user prescribes the num...

Image Clustering Using Graph Cuts in LAB Color Space

In this paper, we developed a novel approach that provides effective and robust segmentation of color images. It preprocesses an image by converting color information from RGB to CIE L*a*b* color spaces that CIE L*a*b* color spaces can express color in a device-independent way. CIE L*a*b* color space is better suited to digital image manipulations than the RGB space, because it had removed corr...

Fast Color Quantization via Fuzzy Clustering

Spatial Distribution of Honeybee Forage based on Color Satellite Image Segmenting using K-Mean Clustering

Beekeeping plays an important role in increasing and diversifying the incomes of many rural communities in Kingdom of Saudi Arabia. However, despite the region’s relatively good rainfall, which result in better forage conditions, bees and beekeepers are greatly affected by seasonal shortages of bee forage. Because of these shortages, beekeepers must continually move their colonies in search of ...

Color Segmentation of Images Using K-Means Clustering With Different Color Spaces