New approach for hierarchical classifier training and multi-level image annotation

Authors:
Jianping Fan;Yuli Gao;Hangzai Luo;Shin'ichi Satoh
Affiliations:
Dept. of Computer Science, UNC-Charlott;Dept. of Computer Science, UNC-Charlott;Dept. of Computer Science, UNC-Charlott;National Institute of Informatics, Tokyo, Japan
Venue:
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Year:
2008

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Abstract

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.