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.
The second method, Clustering Based Multi-Label Classification (CBMLC), groups the training instances into a userspecified number of clusters, and trains a multi-label classification model for each cluster REF .
Clustering based multi-label classification for image annotation and retrieval
2009 IEEE International Conference on Systems, Man and Cybernetics
2009 IEEE International Conference on Systems, Man and Cybernetics
Computer Science