Interest point detection in images using complex network analysis

This paper presents a new application of complex network theory and tools to digital image analysis and computer vision problems in order to detect interest points in digital images. We associate a weighted geometrical and fast computable complex network to each image and then we propose two different methods to locate these feature points based on both local and global (spectral) centrality measures of the corresponding network.

► This paper presents a novel method for computing interest points of digital images by using some centrality measures of complex networks.
► Some analytical results are shown that illustrate the interplay between the global scale centrality measures and the local scale parameters.
► Several computational test are performed in order to compare the new algorithm presented with the classic interest point detectors known in the literature.