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Three-Dimensional Model Retrieval Using Dynamic Multi-Descriptor Fusion
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作者 Jau-Ling Shih Chang-Hsing Lee +1 位作者 yao-wen hou Po-Ting Yen 《Journal of Electronic Science and Technology》 CAS CSCD 2017年第2期169-177,共9页
In this paper, we propose a dynamic multi-descriptor fusion (DMDF) approach to improving the retrieval accuracy of 3-dimensional (3D) model retrieval systems. First, an independent retrieval list is generated by u... In this paper, we propose a dynamic multi-descriptor fusion (DMDF) approach to improving the retrieval accuracy of 3-dimensional (3D) model retrieval systems. First, an independent retrieval list is generated by using each individual descriptor. Second, we propose an automatic relevant/irrelevant models selection (ARMS) approach to selecting the relevant and irrelevant 3D models automatically without any user interaction. A weighted distance, in which the weight associated with each individual descriptor is learnt by using the selected relevant and irrelevant models, is used to measure the similarity between two 3D models. Furthermore, a descriptor-dependent adaptive query point movement (AQPM) approach is employed to update every feature vector. This set of new feature vectors is used to index 3D models in the next search process. Four 3D model databases are used to compare the retrieval accuracy of our proposed DMDF approach with several descriptors as well as some well-known information fusion methods. Experimental results have shown that our proposed DMDF approach provides a promising retrieval result and always yields the best retrieval accuracy. 展开更多
关键词 Index Terms--Three-dimensional (3D) model retrieval automatic relevant/irrelevant models selection (ARMS) feature re-weighting (FRW) query point movement (QPM).
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