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Title: Clustering Based Multi-Label Classification for Image Annotation and Retrieval
Author(s): G. Nasierding, G. Tsoumakas, A. Kouzani.
Availability: Click here to download the PDF (Acrobat Reader) file (6 pages).
Appeared in: 2009 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, 2009.
Abstract: 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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