Patent Document ID: 8232996
Application ID: 12468423

Base Claim:
1. An image learning method comprising: performing a segmentation operation on a first image having annotations to segment the first image into one or more image regions; extracting image feature vectors and text feature vectors from all the image regions to obtain an image feature matrix and a text feature matrix; projecting the image feature matrix and the text feature matrix into a sub-space so as to maximize covariance between an image feature and a text feature, thereby obtaining the projected image feature matrix and the text feature matrix; storing the projected image feature matrix and the text feature matrix; establishing first links between the image regions based on the projected image feature matrix; establishing second links between the first image and the image regions based on a result of the segmentation operation; establishing third links between the first image and the annotations based on the first image having the annotations; establishing fourth links between the annotations based on the projected text feature matrix; calculating weights of all the links; obtaining a graph showing a triangular relationship between the first image, the image regions, and the annotations based on all the links and the weights of the links corresponding to the links; and providing a storage device and a processor, and wherein the step of storing the projected image feature and text feature matrices includes the step of storing information in the storage device, and wherein the step of calculating weights includes the step of using the processor.

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Claim 4:
4. The image learning method according to claim 1 , wherein the sub-space is a canonical covariance sub-space.