Abstract
3D medical model is widely used in many fields such as surgery and medical scene construction. How to find the target model in a large number of 3D models is an important research topic in the field of 3D model retrieval. Due to the existence of semantic gap, the semantic-based method is the current research focus of 3D model retrieval. In this paper, we proposed a semantic-based LDA for automatic 3D medical model annotation and retrieval method. Firstly, we construct semantic features of 3D medical model according to relevance feedback and a small amount of artificial annotation. Then we use the LDA method based on semantic features to obtain latent topic distribution of 3D medical model. Finally, the topic distribution results are applied to automatic annotation of 3D model. Experimental results show that compared with the conventional method of content-based 3D model retrieval, the method can improve the accuracy of 3D medical model automatic annotation and retrieval.
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Acknowledgments
Our work is supported by the National Natural Science Foundation of China under Grant No. 61303132.
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Wang, X., Gu, F., Xiao, W. (2016). 3D Medical Model Automatic Annotation and Retrieval Using LDA Based on Semantic Features. In: Yin, X., Geller, J., Li, Y., Zhou, R., Wang, H., Zhang, Y. (eds) Health Information Science. HIS 2016. Lecture Notes in Computer Science(), vol 10038. Springer, Cham. https://doi.org/10.1007/978-3-319-48335-1_10
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DOI: https://doi.org/10.1007/978-3-319-48335-1_10
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