<p>An image identification method for classifying block images of input image data into one of the multiple predetermined categories according to feature quantity in each block image; the method includes an image production step of dividing image data into multiple blocks to produce block images, an image feature quantity processing step of processing the feature quantity of each block image by their color space information and frequency component, a separating hyperplane processing step of learning separating hyperplanes that indicate boundaries of each category by reading in training data image that have labeled categories for each block and processing image feature quantity for each block of an training data image, and a category classification step of classifying respective block image to a category according to the distance from the separating hyperplane of each category by executing the block image production step and the image feature quantity processing step for a newly acquired image to obtain the image feature quantity of block images.</p>
