Color Image Segmentation Using Fuzzy C-Means (FCM) Clustering Method

In this article, we will implement color image segmentation using the Fuzzy C-Means (FCM) clustering method. To better characterize image features, we can experiment with different feature vectors. The algorithm typically involves calculating the Euclidean distance between image pixels and cluster centers in a multi-dimensional feature space. We can employ RGB color channels, HSV color space components, or texture features as input vectors for the clustering process. This approach helps us achieve more comprehensive image understanding and extract more valuable information. By utilizing multiple feature vectors in the FCM implementation, we can obtain more accurate and detailed segmentation results, ultimately improving our image analysis and interpretation capabilities. The FCM algorithm iteratively updates membership values and cluster centers until convergence, with key parameters including the fuzziness exponent and number of clusters that can be optimized for different image types.