Paper Title:
Medical Image Retrieval Based on Semi-Supervised Learning
  Abstract

Among various content-based image retrieval (CBIR) methods based on active learning, support vector machine(SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. Furthermore, it’s difficult to collect vast amounts of labeled data and easy for unlabeled data to image examples. Therefore, it is necessary to define conditions to utilize the unlabeled examples enough. This paper presented a method of medical images retrieval about semi-supervised learning based on SVM for relevance feedback in CBIR. This paper also introduced an algorithm about defining two learners, both learners are re-trained after every relevance feedback round, and then each of them gives every image in a rank. Experiments show that using semi-supervised learning idea in CBIR is beneficial, and the proposed method achieves better performance than some existing methods.

  Info
Periodical
Advanced Materials Research (Volumes 108-111)
Edited by
Yanwen Wu
Pages
201-206
DOI
10.4028/www.scientific.net/AMR.108-111.201
Citation
H. Liu, C. M. Zhang, H. Han, "Medical Image Retrieval Based on Semi-Supervised Learning", Advanced Materials Research, Vols. 108-111, pp. 201-206, 2010
Online since
May 2010
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Price
$32.00
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