Image Segmentation using a Weighted Kernel PCA Approach to Spectral Clustering

In classical graph-based image segmentation, a data-driven matrix is constructed representing similarities between every pair of pixels. The eigenvectors of such matrices contain relevant information about the clusters present on the image. An approach to image segmentation using spectral clustering with out-of-sample extensions is presented. This approach is based on the weighted kernel PCA framework. An advantage of the proposed method is the possibility to train and validate the clustering model on subsampled parts of the image to be segmented. The cluster indicators for the remaining pixels can then be inferred using the out-of-sample extension. This subsampling scheme can be used to reduce the computation time of the segmentation. Simulation results with grayscale and color images show improvements in terms of computation times together with visually appealing clusters