Semi-supervised emotional classification of color images by learning from cloud

Classification of images based on the feelings generated by each image in its reviewers is becoming more and more popular. Due to the difficulty of gathering training data, this task is intrinsically a small-sample learning problem. Hence, the results produced by most existing solutions are less accurate. In this paper, we propose the semi-supervised hierarchical classification (SSHC) algorithm for emotional classification of color images. We extract three groups of features for each classification task and use those features in a two-level classification model that is based on the support vector machine (SVM) and Adaboost technique. To enlarge the training dataset, we employ each training image to retrieve similar images from the Internet cloud and jointly use the manually labeled small dataset and retrieved large but unlabeled dataset to train a classifier via semi-supervised learning. We have evaluated the proposed algorithm against the fuzzy similarity-based emotional classification (FSBEC) algorithm and another supervised hierarchical classification algorithm that does not learn from online images in three bi-class classification tasks, including warm vs. cool, light vs. heavy and static vs. dynamic. Our pilot results suggest that, by learning from the similar images archived in the Internet cloud, the proposed SSHC algorithm can produce more accurate emotional classification of color images.