Empirical Study of Multi-label Classification Methods for Image Annotation and Retrieval

@article{Nasierding2010EmpiricalSO,
  title={Empirical Study of Multi-label Classification Methods for Image Annotation and Retrieval},
  author={Gulisong Nasierding and Abbas Z. Kouzani},
  journal={2010 International Conference on Digital Image Computing: Techniques and Applications},
  year={2010},
  pages={617-622}
}
This paper presents an empirical study of multi-label classification methods, and gives suggestions for multi-label classification that are effective for automatic image annotation applications. The study shows that triple random ensemble multi-label classification algorithm (TREMLC) outperforms among its counterparts, especially on scene image dataset. Multi-label k-nearest neighbor (ML-kNN) and binary relevance (BR) learning algorithms perform well on Corel image dataset. Based on the overall… CONTINUE READING
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