Weight Fuzzy C-means Algorithm Based on 1D histgram

资 源 简 介

Weight Fuzzy C-means Algorithm Based on 1D histgram

详 情 说 明

Weight Fuzzy C-means Algorithm Based on 1D Histogram

Traditional Fuzzy C-means (FCM) clustering is widely used in image segmentation, but it often struggles with uneven data distributions. The Weight Fuzzy C-means Algorithm introduces an adaptive weighting mechanism based on a 1D histogram to enhance clustering accuracy.

Histogram Preprocessing The 1D histogram of an image (e.g., grayscale or color intensity) is first computed. This histogram serves as a compact representation of pixel distribution, reducing computational overhead compared to raw pixel data.

Weight Assignment Unlike standard FCM, the weighted version assigns importance values to histogram bins. High-frequency bins (dominant intensities) receive higher weights, ensuring they influence cluster centroids more significantly.

Modified FCM Optimization The clustering objective function incorporates bin weights, adjusting centroid updates during iteration. This helps mitigate noise and outliers by de-emphasizing sparse histogram regions.

Applications in Segmentation The weighted approach excels in medical imaging (e.g., MRI) and remote sensing, where intensity variations are critical but unevenly distributed. By leveraging histogram weights, it achieves smoother boundaries and preserves subtle features.

This variant balances computational efficiency (via histogram compression) with precision (via adaptive weighting), making it ideal for real-world scenarios with complex intensity distributions.