One of the important issues in the automatic processing of external images is how to divide these images for the purpose of recognizing something in them. The special characteristics of these images, including color diversity, different light effects, the presence of co
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One of the important issues in the automatic processing of external images is how to divide these images for the purpose of recognizing something in them. The special characteristics of these images, including color diversity, different light effects, the presence of colored shadows, many texture details, and the existence of small and heterogeneous objects, make the problem of segmentation of external images, especially color segmentation, face serious challenges. In previous researches, a method based on the k-means clustering algorithm was proposed in a multi-accuracy bed for color clustering of external images for the purpose of primary segmentation. This method uses deliberate blurring of image textural details and removal of specific classes in blurred images and then added The classification of classes in images with higher accuracy showed a suitable performance for the initial segmentation of these images in comparison with the normal k-means method. In this article, an image-adaptive method using the ring histogram of the dark color to identify specific classes in blurred images in the bed is presented. It has been proposed with double precision. The efficiency of this algorithm has been investigated with the help of a supervised evaluation method on two databases of external images, which shows a 20% reduction in pixel error in segmentation, as well as a 30% higher accuracy and speed in the convergence of the clustering algorithm, indicating a higher quality. The proposed method is better than the normal method.
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