Interest point detection based on Laplacian energy of local image network

A point of interest is the characteristic of an image which can be robustly detected due to its well-defined position. The points of interest should be easily computable and are invariant to transformations in the image domain. This paper presents a novel interest point detector based on a graph centrality measure defined over the Laplacian energy of the local image network. The proposed method considers the local circular neighborhood of each pixel and associates this with an undirected graph or network. Euclidean distance is employed to connect pixels to form graph edges. The Laplacian energy of the graph will drop when a vertex is removed from it. This relative reduction indicates the significance of that vertex in the network. Graph vertices having higher values of Laplacian centrality are identified as central most nodes. This measure of pixel significance indicates the presence of interest points. The effectiveness and robustness of the proposed approach in detecting local points of high entropy values are demonstrated by the experiments conducted on standard datasets. The visual assessment of the identified interest points correlates with the coverage value calculated. Moreover, the proposed method shows stable results in the presence of geometric transformations such as rotation, viewpoint changes, and zooms. These results provide substantial evidence of the proposed interest point detector's utility in image processing applications such as image registration, object recognition, and image matching, etc.