1. An image processing apparatus comprising: an image acquisition unit that acquires an input image that is to be subjected to processing; a foreground map generation unit that generates a foreground map, which indicates a foreground region and a background region in the input image, based on the input image; a learning sample extraction unit that extracts data of a positive learning sample from the foreground region in the input image and extracts data of a negative learning sample from the background region in the input image, based on the foreground map; a classifier learning unit that performs learning of a plurality of classifiers using the positive and negative learning samples extracted from the input image; a strong classifier generation unit that generates a strong classifier by combining the plurality of learned classifiers; and a saliency map generation unit that generates a final saliency map of the input image using the strong classifier.
2. The image processing apparatus according to claim 1 , wherein the foreground map is a map having values indicating foreground-likelihood for each pixel or each super-pixel of the image, and wherein the learning sample extraction unit extracts a pixel or a super-pixel having a value indicating foreground-likelihood that is greater than a first threshold value as a positive learning sample, and extracts a pixel or a super-pixel having a value indicating foreground-likelihood that is less than a second threshold value as a negative learning sample.