Distributed Cluster Based 3D Model Retrieval with Map-Reduce

• 作者: Dechao Wu Panjing Li Xiaohong Liu Yuhang Chen Zhijian Qu
• 来源期刊: 电脑和通信(英文)
• 学科: 工学
• 关键词: MAP-REDUCE 3D Model RETRIEVAL K-MEANS FEATURE EXTRACTION
• 年，卷（期）: dnhtxyw_2018,（5）
• 页码范围: 83-93
• 页数: 11页
• 分类号: TP39

摘要

View-based 3D model retrieval methods are attracted intensive research attentions due to the high expression and stable features. In the paper, the bag-of-words (BOW) standardization based SIFT feature were extracted from three projection views of a 3D model, and then the distributed K-means cluster algorithm based on a Hadoop platform was employed to compute feature vectors and cluster 3D models. In order to get precise initial cluster center, the maximum and minimum principle based Canopy algorithm was also presented. The similarity of models was determined by the distance between the query model and each cluster center, and the cluster which nearest to the query model will be return as retrieval results. The simulations indicated that the proposed method had good results in terms of 3D model retrieval accuracy and retrieval time efficiency.