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- W2049715255 abstract "The popularization of 3D scanning, 3D modeling and 3D printing had created an urgent demand for effective and useful 3D model retrieval methods and systems. In this paper, a novel feature fusing method by using fuzzy clustering is proposed for 3D model retrieval. Given two 3D descriptors, where the dimension of one is short and of the other is long, fuzzy clustering is conducted for the short feature dataset in advance, in order to generate a weighted index by classifying the features into several clusters, different clusters are given different weights based on the centroid of the cluster with the query model. Then, the long feature is used for similarity matching embedded within the generated weighted index. Experimental results demonstrate that the retrieval performance can be highly improved by using the proposed feature fusion scheme." @default.
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- W2049715255 date "2015-08-19" @default.
- W2049715255 modified "2023-09-24" @default.
- W2049715255 title "Feature fusion for 3D model retrieval based on fuzzy clustering" @default.
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- W2049715255 doi "https://doi.org/10.1145/2808492.2808505" @default.
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