Clustering based multi-label classification for image annotation and retrieval
Nasierding, Gulisong, Tsoumakas, Grigorios and Kouzani, Abbas Z. 2009, Clustering based multi-label classification for image annotation and retrieval, in SMC 2009 : Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, IEEE, Piscataway, N. J., pp. 4514-4519, doi: 10.1109/ICSMC.2009.5346902.
This paper presents a novel multi-label classification framework for domains with large numbers of labels. Automatic image annotation is such a domain, as the available semantic concepts are typically hundreds. The proposed framework comprises an initial clustering phase that breaks the original training set into several disjoint clusters of data. It then trains a multi-label classifier from the data of each cluster. Given a new test instance, the framework first finds the nearest cluster and then applies the corresponding model. Empirical results using two clustering algorithms, four multi-label classification algorithms and three image annotation data sets suggest that the proposed approach can improve the performance and reduce the training time of standard multi-label classification algorithms, particularly in the case of large number of labels.
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