Introduction to Algorithms
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Correlative multi-label video annotation
Proceedings of the 15th international conference on Multimedia
Extracting shared subspace for multi-label classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Scene Discovery by Matrix Factorization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Multi-label image annotation based on neighbor pair correlation chain
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
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In the real world, images always have several visual objects instead of only one, which makes it difficult for conventional object recognition methods to deal with them. In this paper, we present a topologically sorted classifier chain method for learning images with multi-label. We first provide a means of generating a topo-logically sorted label chain ordering by employing a topological sort algorithm and then apply the chain ordering to the classifier chain model proposed by [1] to classify multi-label images. Our method can capture the correlations between labels very effectively due to the sorted label chain ordering and the advantages brought by classifier chain method. We evaluate the proposed method on Corel dataset and demonstrate the micro and macro F1 measures superior to the state-of-the-art methods.