一种SVM学习框架下的Web3D轻量级模型检索算法

doi:10.3969/j.issn.0372-2112.2019.01.012

电子学报 ›› 2019, Vol. 47 ›› Issue (1): 92-99.DOI: 10.3969/j.issn.0372-2112.2019.01.012

一种SVM学习框架下的Web3D轻量级模型检索算法

周文, 贾金原

同济大学软件学院, 上海 201804

收稿日期:2017-10-09 修回日期:2018-02-15 出版日期:2019-01-25
- 作者简介:
- 周文男,1984年2月出生于安徽铜陵,现为同济大学博士研究生,主要研究方向:虚拟现实、三维模型检索等.E-mail:zhouwen606@126.com;贾金原 男,1963年11月出生于山东德州,现为同济大学教授,博士生导师,主要研究方向:虚拟现实、轻量级三维建模等.E-mail:jyjia@tongji.edu.cn
- 基金资助:
- 中央高校基本科研业务费-学科交叉类项目重点类项目A类 (No.0200219153）

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

ZHOU Wen, JIA Jin-yuan

School of Software Engineering, Tongji University, Shanghai 201804, China

Received:2017-10-09 Revised:2018-02-15 Online:2019-01-25 Published:2019-01-25
- Supported by:
- Fundamental Research Funds for the Central Universities - Key Program of Interdisciplinary Project (Class A) (No.0200219153)

摘要/Abstract

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.

关键词: 基于草图的三维模型检索, 支持向量机, 简化, 梯度直方图, 特征字典

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.

Key words: sketch-based shape retrieval, SVM, simplified, HOG, features dictionary

中图分类号:

TP391.3

周文, 贾金原. 一种SVM学习框架下的Web3D轻量级模型检索算法[J]. 电子学报, 2019, 47(1): 92-99.

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.

参考文献

[1] KETTNER L.Using generic programming for designing a data structure for polyhedral surfaces[J].Computational Geometry,1999,13(1):65-90.
[2] HOPPE H.Progressive meshes[A].Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques SIGGRAPH[C].USA:ACM,1996.99-108.
[3] WEN Lai-xiang,JIA J,et al.LPM:Lightweight progressive meshes towards smooth transmission of Web3D media over Internet[A].Proceedings of ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry[C].USA:ACM,2014.99-104.
[4] MORIGI S,RUCCI M.Multilevel mesh simplification[J].Vis Computer,2014,30(5):479-492.
[5] EITZ M,HILDEBRAND K,BOUBEKEUR T,ALEXA M.Sketch-based 3D shape retrieval[A].2010 Proceedings of SIGGRAPH[C].USA:ACM,2010.5-6.
[6] EITZ M,HILDEBRAND K,BOUBEKEUR T,ALEXA M.An evaluation of descriptors for large-scale image retrieval from sketched feature lines[J].Computer Graph,2010,34(2):482-498.
[7] LI B,JOHAN H.View context:a 3D model feature for retrieval[A].Proceedings of 2010 International Multimedia Modeling Conference[C].Germany:Springer,2010.185-195.
[8] LI B,JOHAN H.Sketch-based 3D model retrieval by incorporating 2D-3D alignment[J].Multimedia Tools Appl,2013,65(9):363-385.
[9] LOWE D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60:91-110.
[10] JEGOU H,DOUZE M,SCHMID C,PéREZ P.Aggregating local descriptors into a compact image representation[A].Proceedings of the 23rd IEEE Conference on Computer Vision and Pattern Recognition[C].USA:IEEE,2010.3304-3311.
[11] OHBUCHI R,FURUYA T.Scale-weighted dense bag of visual features for 3D model retrieval from a partial view 3D model[A].Proceedings of 2009 IEEE ICCV Workshop on Search in 3D and Video[C].USA:ACM,2009.63-70.
[12] WANG F,KANG L,LI Y.Sketch-based 3D shape retrieval using convolutional neural networks[A].Proceedings of IEEE Conference on Computer Vision and Pattern Recognition[C].USA:IEEE,2015.1875-1883.
[13] DALAL N,TRIGGS B.Histograms of oriented gradients for human detection[A].Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C].USA:IEEE Computer Society,2005.886-893.
[14] EITZ M,HAYS J,ALEXA M.How do humans sketch objects?[J].ACM Trans Graph,2012,44(31):1-10.
[15] EITZ M,HILDEBRAND K,BOUBEKEUR T,ALEXA M.Sketch-based image retrieval:benchmark and bag-of-features descriptors[J].IEEE Trans Visual Computer Graph,2011,17(5):1624-1636.
[16] DUTAGACI H,CHEUNG C P,GODIL A.A benchmark for best view selection of 3D objects[A].Proceedings of 2010 ACM Workshop on 3D Object Retrieval[C].USA:ACM,2010.45-50.
[17] 刘志,马骏,潘翔.特征自适应的三维模型最优视点提取[J].计算机辅助设计与图形学学报,2014,26(10):1774-1780 LIU Zhi,MA J,PAN Xiang.Optimal viewpoint extraction algorithm for three-dimensional model based on features adaption[J].Journal of Computer-Aided Design & Computer Graphics,2014,26(10):1774-1780.(in Chinese)
[18] ZHAO L,LIANG S,JIA J.Learning best views of 3D shapes from sketch contour[J].The Visual Computer,2015,31(6):765-774.
[19] LEE C H,VARSHNEY A,Jacobs D W.Mesh saliency[J].ACM Transactions on Graphics,2005,24(3):659-666
[20] LIU H,ZHANG L,HUANG H.Web-image driven best views of 3D shapes[J].The Visual Computer,2012,28(3):279-287.
[21] SHILANE P,MIN P,KAZHDAN M.The Princeton shape benchmark[A].Proceedings of 2004 International Shape Modeling[C].USA:IEEE Computer Society,2004.167-178.
[22] CHEN D,TIAN X,SHEN Y.On visual similarity based 3D model retrieval[J].Computer Graphics Forum,2010,22(3):223-232.
[23] LI Bo,LU Yi-juan,GODIL Afzal.A comparison of methods for sketch-based 3D shape retrieval[J].Computer Vision and Image Understanding,2014,119(2):57-80.

一种SVM学习框架下的Web3D轻量级模型检索算法

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

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