A new re-ranking method using enhanced pseudo-relevance feedback for content-based medical image retrieval
Huang, Yonggang, Zhang, Jun, Zhao, Yongwang and Ma, Dianfu 2012, A new re-ranking method using enhanced pseudo-relevance feedback for content-based medical image retrieval, IEICE transactions on information and systems, vol. E95-D, no. 2, pp. 694-698.
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A new re-ranking method using enhanced pseudo-relevance feedback for content-based medical image retrieval
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.
Language
eng
Field of Research
089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences