Abstract
In this paper, we have proposed a novel algorithm to achieve automatic multi-level image annotation by incorporating concept ontology and multi-task 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.
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References
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Fan, J., Gao, Y., Luo, H., Satoh, S. (2008). New Approach for Hierarchical Classifier Training and Multi-level Image Annotation. In: Satoh, S., Nack, F., Etoh, M. (eds) Advances in Multimedia Modeling. MMM 2008. Lecture Notes in Computer Science, vol 4903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77409-9_5
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DOI: https://doi.org/10.1007/978-3-540-77409-9_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77407-5
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