18. A method for generating an image segmentation system, comprising: receiving a set of training images in which semantic regions of each image have been identified and labeled with a sematic class selected from a predefined set of semantic classes; for each of the training images: segmenting the training image in an unsupervised manner into a hierarchy of regions, without regard to the labels; extracting a region feature vector for each of the regions in the hierarchy of regions; and generating a triplet feature vector for each of a plurality of triplet hierarchical structures in the hierarchy, each triplet hierarchical structure comprising a parent region which is the union of two respective child regions, each triplet feature vector being based on the region feature vectors of exactly three regions comprising the respective parent region and the two respective child regions; training a hierarchical classifier model for at least one of the sematic classes based on the semantic region labels of the semantic regions in which pixels of the triplet hierarchical structures lie and the respective triplet feature vectors of the triplet hierarchical structures; optionally, training a region classifier model for the at least one of the sematic classes based on the semantic region labels and the region feature vectors; whereby, when a new image to be segmented according to semantic class is received, a labeling component is able to label a semantic region of the image according to a class in the set of classes based on triplet feature vectors for triplet hierarchical structures each comprising a parent and its two child regions of a hierarchy that are generated by unsupervised segmentation of the input image.
19. The method of claim 18 , wherein the method includes training of the region classifier model.