Hierarchical learning of multi-task sparse metrics for large-scale image classification

In this paper, a novel approach is developed to learn a tree of multi-task sparse metrics hierarchically over a visual tree to achieve a fast solution to large-scale image classification, where an enhanced visual tree is first learned to organize large numbers of image categories hierarchically in a coarse-to-fine fashion. Over the visual tree, a tree of multi-task sparse metrics is learned hierarchically by: (a) performing multi-task sparse metric learning over the sibling child nodes under the same parent node to explicitly separate their commonly-shared metric from their node-specific metrics; and (b) propagating the node-specific metric for the parent node to its sibling child nodes (at the next level of the visual tree), so that more discriminative metrics can be learned for controlling inter-level error propagation effectively. We have evaluated our hierarchical multi-task sparse metric learning algorithm over three different image sets and the experimental results demonstrated that our hierarchical multi-task sparse metric learning algorithm can obtain better performance than the state-of-the-art algorithms on large-scale image classification.