A graph-based approach for feature extraction and segmentation of multimodal images

In the past few years, graph-based methods have proven to be a useful tool in a wide variety of energy minimization problems [1]. In this paper, we propose a graph-based algorithm for feature extraction and segmentation of multimodal images. By defining a notion of similarity that integrates information from each modality, we merge the different sources at the data level. The graph Laplacian then allows us to perform feature extraction and segmentation on the fused dataset. We apply this method in a practical example, namely the segmentation of optical and lidar images. The results obtained confirm the potential of the proposed method.