hierarchical classification for automatic image annotation
In this paper, a hierarchical classification framework has been proposed for bridging the semantic gap effectively and achieving multi-level image annotation automatically. First, the semantic gap between the low-level computable visual features and users' real information needs is partitioned into four smaller gaps, and multiple approachesallare proposed to bridge these smaller gaps more effectively. To learn more reliable contextual relationships between the atomic image concepts and the co-appearances of salient objects, a multi-modal boosting algorithm is proposed. To enable hierarchical image classification and avoid inter-level error transmission, a hierarchical boosting algorithm is proposed by incorporating concept ontology and multi-task learning to achieve hierarchical image classifier training with automatic error recovery. To bridge the gap between the computable image concepts and the users' real information needs, a novel hyperbolic visualization framework is seamlessly incorporated to enable intuitive query specification and evaluation by acquainting the users with a good global view of large-scale image collections. Our experiments on large-scale image databases have also obtained very positive results.
semantic gap image classification hierarchical boosting concept ontology multi-task learning