New Approach for Hierarchical Classifier Training and Multi-level Image Annotation

In this paper, we have proposed a novel algorithm to achieve automatic multi-level image annotation by incorporating concept ontology and multitask learning for hierarchical image classifier training. To achieve more reliable image classifier training in high-dimensional heterogeneous feature space, a new algorithm is proposed by incorporating multiple kernels for diverse image similarity characterization, and a multiple kernel learning algorithm is developed to train the SVM classifiers for the atomic image concepts at the first level of the concept ontology. To enable automatic multi-level image annotation, a novel hierarchical boosting algorithm is proposed by incorporating concept ontology and multi-task learning to achieve hierarchical image classifier training.