A new re-ranking method using enhanced pseudo-relevance feedback for content-based medical image retrieval
Data(s) |
01/02/2012
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Resumo |
We propose a novel re-ranking method for content-based medical image retrieval based on the idea of pseudo-relevance feedback (PRF). Since the highest ranked images in original retrieval results are not always relevant, a naive PRF based re-ranking approach is not capable of producing a satisfactory result. We employ a two-step approach to address this issue. In step 1, a Pearson's correlation coefficient based similarity update method is used to re-rank the high ranked images. In step 2, after estimating a relevance probability for each of the highest ranked images, a fuzzy SVM ensemble based approach is adopted to re-rank the images. The experiments demonstrate that the proposed method outperforms two other re-ranking methods.<br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
Denshi Jouhou Tsuushin Gakkai |
Relação |
http://dro.deakin.edu.au/eserv/DU:30047014/zhang-newreranking-2012.pdf http://hdl.handle.net/10.1587/transinf.E95.D.694 |
Direitos |
2012, The Institute of Electronics, Information and Communication Engineers |
Palavras-Chave | #CBIR #fuzzy SVM ensemble #re-ranking #similarity update |
Tipo |
Journal Article |