Abstract
The current image retrieval systems are almost based on content, and facing the main problem of semantic gap between low level features and high level semantic. So the relevance feedback technology is used to solve this problem. In this paper, we propose a medical image retrieval system based on relevance feedback framework. In the framework, Region of Interest (ROI) is extracted in the preprocessing as the semantic information of medical images, and then the Genetic Algorithm is designed for ROI clustering. According to user’s feedback information, the Diverse Density algorithm proposed in the Multiple Instance Learning Framework is adopted to capture user’s real intention and realize effectively medical image relevance. Experimental results show that our algorithm has higher precision and recall ratio.
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Wang, R., Pan, H., Han, Q., Gu, J., Li, P. (2012). Medical Image Retrieval Method Based on Relevance Feedback. In: Zhou, S., Zhang, S., Karypis, G. (eds) Advanced Data Mining and Applications. ADMA 2012. Lecture Notes in Computer Science(), vol 7713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35527-1_54
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DOI: https://doi.org/10.1007/978-3-642-35527-1_54
Publisher Name: Springer, Berlin, Heidelberg
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