Food image segmentation using an improved kernel fuzzy C-means algorithm

In this work, an improved kernel fuzzy c-means (KFCM) algorithm was developed for food image segmentation. Besides the three color components of RGB (red, green, and blue) space, three image texture features were first extracted to represent the spatial contextual information of each image pixel, including polarity, anisotropy, and local contrast. After that, the kernel trick was integrated into the conventional fuzzy c-means (FCM) algorithm to transform the feature vectors to a higher-dimensional space so that the vectors could be linearly separated within this space. Since the original KFCM method was computationally infeasible for real-time implementation, a new scheme was developed to reduce its time and memory complexity by reorganizing the calculations of kernel, membership, and cluster centroid matrices and wiping out the storage of the membership matrix. The experimental results indicate that the algorithm has attractive strengths in generality and computational efficiency.