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• [P842] Clustering Based Multi-Label Classification for Image Annotation and Retrieval
  Author(s): Gulisong Nasierding, Grigorios Tsoumakas, Abbas Z. Kouzani
  
  

  Abstract
  This paper presents a novel multi-label classi¡¥cation frame-work 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 classier from the data of each cluster. Given a new test instance, the framework¡¥rst ¡¥nds the nearest cluster and then applies the corresponding model. Empirical results using two clustering algorithms, four multi-label classi¡¥cation 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 classi¡¥cation algorithms, particularly in the case of large number of labels.

  
   

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