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Clustering based multi-label classification for image annotation and retrieval
conference contribution
posted on 2009-01-01, 00:00 authored by Gulisong NasierdingGulisong Nasierding, G Tsoumakas, Abbas KouzaniAbbas KouzaniThis 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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Event
IEEE International Conference on Systems, Man, and Cybernetics (2009 : San Antonio, Texas)Pagination
4514 - 4519Publisher
IEEELocation
San Antonio, TexasPlace of publication
Piscataway, N. J.Publisher DOI
Start date
2009-10-11End date
2009-10-14ISSN
1062-922XISBN-13
9781424427932Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2009, IEEETitle of proceedings
SMC 2009 : Proceedings of the IEEE International Conference on Systems, Man and CyberneticsUsage metrics
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