Empirical study of multi-label classification methods for image annotation and retrieval
Nasierding, Gulisong and Kouzani, Abbas Z. 2010, Empirical study of multi-label classification methods for image annotation and retrieval, in DICTA 2010 : Proceedings of the Digital Image Computing : Techniques and Application, DICTA, Sydney, NSW, pp. 617-622.
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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 evaluation results, examples are given to show label prediction performance for the algorithms using selected image examples. This provides an indication of the suitability of different multi-label classification methods for automatic image annotation under different problem settings.
Field of Research
090609 Signal Processing 080109 Pattern Recognition and Data Mining
Socio Economic Objective
890301 Electronic Information Storage and Retrieval Services
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