10. The image automatic annotation method according to claim 7 , wherein the sub-space is a canonical covariance sub-space.
7. An image automatic annotation method for making an annotation on an input second image, the image automatic annotation method comprising the learning method according to claim 1 , a preliminary processing step, a graph update step, and an annotation step, wherein the preliminary processing step includes: receiving the second image; performing the segmentation operation on the second image to segment the second image into one or more image regions; extracting image feature vectors from all the image regions to obtain an image feature matrix of the second image; and projecting the image feature matrix into the sub-space to obtain a projected image feature matrix of the second image, the graph update step includes: establishing fifth links between the image region nodes of the second image and the image region nodes in the graph based on the projected first image feature matrix and the second image feature matrix; establishing sixth links between the second image and the image region nodes based on a result of the segmentation operation; determining weights of the links of the fifth links and the sixth links; and updating the graph based on the fifth links and the sixth links and the weights of the links corresponding to the fifth links and the sixth links, and the annotation step includes: generating a restart vector corresponding to the second image; acquiring a predetermined number of annotations most closely related to the second image with a random walk with restart; and making the annotations on the second image using keywords corresponding to the predetermined number of annotations.