Hierarchical learning of multi-task sparse metrics for large-scale image classification. (July 2017)

• Record Type: Journal Article
• Authors: Zheng, Yu; Fan, Jianping; Zhang, Ji; Gao, Xinbo
• Is Part Of: Pattern recognition. Volume 67(2017:Jul.)
• Page Start: 97
• Page End: 109
• Publication Date: 2017-07
• Subjects: Hierarchical multi-task sparse metric learning -- Visual tree -- Large-scale image classification; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.01.029
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: An enhanced hierarchical visual tree is constructed to organize large numbers of image categories and automatically identify the inter-related tasks for multi-task sparse metric learning. A new objective function is define for multi-task sparse metric learning. A top-down approach is developed for supporting hierarchical learning of a tree of multi-task sparse metrics over the enhanced visual tree. Abstract: 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.