Web3D Lightweight for Sketch-Based Shape Retrieval Using SVM Learning Algorithm

ZHOU Wen; JIA Jin-yuan

doi:10.3969/j.issn.0372-2112.2019.01.012

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Web3D Lightweight for Sketch-Based Shape Retrieval Using SVM Learning Algorithm

更新时间：2025-07-16

- Web3D Lightweight for Sketch-Based Shape Retrieval Using SVM Learning Algorithm
- Acta Electronica Sinica Vol. 47, Issue 1, Pages: 92-99(2019)
- 作者机构：
  
  同济大学软件学院,上海,201804
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2019.01.012
  CLC： TP391.3
- Published Online：25 January 2019，
  
  Published：2019
- 稿件说明：
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ZHOU Wen, JIA Jin-yuan. Web3D Lightweight for Sketch-Based Shape Retrieval Using SVM Learning Algorithm[J]. Acta Electronica Sinica, 2019, 47(1): 92-99.
DOI：

ZHOU Wen, JIA Jin-yuan. Web3D Lightweight for Sketch-Based Shape Retrieval Using SVM Learning Algorithm[J]. Acta Electronica Sinica, 2019, 47(1): 92-99. DOI： 10.3969/j.issn.0372-2112.2019.01.012.

摘要

随着Web3D技术的发展，对于互联网检索三维模型的需求越来越迫切，特别是基于草图的模型检索.本文对基于草图的三维模型检索相关技术进行了研究，提出了三维模型轻量化处理算法、基于支持向量机三维模型最佳视点选择算法.本文首先对模型进行简化处理，投影三维模型为多个视点图像.其次，使用支持向量机在草图数据集上学习规则，并根据规则进行相应的视点图像分类，获得最佳视点图像.再次，对视点图像提取梯度直方图特征并进行K-means聚类和索引，减少特征空间，获得三维模型的特征字典.最后，在开源数据集上进行相关的实验并对结果进行分析，相关结果表明方法具有很强鲁棒性、准确性.

Abstract

With the rapid development of Web3D technology

the demand to use and retrieve 3D model is becoming urgent.Especially

sketch-based shape retrieval is very important.In the paper

a framework is proposed

which includes lightweight for shape

SVM-based learning algorithm.In particular

the model is simplified and then projected into multi-views images.Besides

a SVM classifier is used to classify these images.Moreover

histogram of oriented gradient (HOG) features is extracted from the input sketch image.Furthermore

K-means algorithm is used to cluster and index these features in order to generate a features dictionary.Finally

the related experiments are conducted to verify the feasibility of the approach in open source datasets.The result shows that the proposed method is robust and superior

compared with other methods.

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