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.
(Some files may be inaccessible until you login with your DRO credentials)
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.
Field of Research
080109 Pattern Recognition and Data Mining
Socio Economic Objective
890402 Film and Video Services (excl. Animation and Computer Generated Imagery)
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.
Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO.
If you believe that your rights have been infringed by this repository, please contact firstname.lastname@example.org.