摘要
3D模型检索是当前国内外的一个研究热点。针对多模态数据集,提出了一种基于组稀疏编码方法的3D模型检索系统。首先,从表征三维模型的二维视图中提取SIFT特征。在此基础上,利用LDA(latent dirichlet allocation)模型生成3D模型的视图主题分布,并将此分布作为特征以表征三维模型。最后,采用组稀疏编码算法计算不同3D模型间的相似性,从而解决模型的检索问题。实验结果证明了所提出的检索算法的有效性。
3D model retrieval is a research focus at home and abroad .In this paper ,we propose a novel 3D object retrieval system via group sparse coding based on multi-model dataset .First ,we extract SIFT feature from a series of 2D model images which recorded from each 3D model .Then the visual topic distribution generated by LDA （latent dirichlet allocation） is selected to represent each 3D model .Finally ,the sparse coding algorithm is utilized to compute the similarity between different 3D models as to solve the retrieval problem .Experimental results demonstrate the effectiveness of the proposed algorithm .
出处
《电子测量技术》
2016年第6期9-14,共6页
Electronic Measurement Technology
