Morphological PDEs on graphs for saliency detection

Visual saliency is a computational process that seeks to identify the most attention drawing regions from a visual point. In this study, the authors propose a new algorithm to estimate the saliency based on partial difference equations (PDEs) method. A local or non-local graph is first constructed from the geometry of images. Then, the transcription of PDE on graph is done and resolved by using the mean curvature flow that can be used to perform regularisation and the Eikonal equation for segmentation. Finally, an extended region adjacency graph is built, which is extended with a k-nearest neighbour graph, in the mean RGB colour space of each region in order to estimate saliency. The proposed algorithm allows to unify a local or non-local graph processing for saliency computing. Furthermore, it works on discrete data of arbitrary topology. For evaluation, the proposed method is tested on two different datasets and 3D point clouds. Extensive experimental results show the applicability and effectiveness of the proposed algorithm.