Medical Image Retrieval Based on Semi-Supervised Learning

Autor: Cai Ming Zhang, Hua Han, Hui Liu
Rok vydání: 2010
Předmět:
Zdroj: Advanced Materials Research. :201-206
ISSN: 1662-8985
DOI: 10.4028/www.scientific.net/amr.108-111.201
Popis: 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.
Databáze: OpenAIRE