This paper details a novel approach to automatically selecting images which improve camera calibration results. An algorithm is presented which identifies calibration images that inherently improve camera parameter estimates based on their geometric configuration or image network geometry. Analysing images in a more intuitive geometric framework allows image networks to be formed based on the relationship between their world to image homographies. Geometrically, it is equivalent to enforcing maximum independence between calibration images, this ensures accuracy and stability when solving the planar calibration equations. A webcam application using the proposed strategy is presented. This demonstrates that careful consideration of image network geometry, which has largely been neglected within the community, can yield more accurate parameter estimates with less images.