摘要
基于手绘草图的三维模型检索(SBSR)已成为三维模型检索、模式识别与计算机视觉领域的一个研究热点。与传统方法相比,基于卷积神经网络(CNN)的三维深度表示方法在三维模型检索任务中性能优势非常明显。本文提出了一种基于手绘图像融合信息熵和CNN的三维模型检索方法。首先,通过计算模型投影图的信息熵得到模型的代表性视图,并将代表性视图经过边缘检测等处理得到三维模型投影图的轮廓图像;然后,将轮廓图像和手绘草图输入到CNN中提取特征描述子,并进行特征匹配。本文方法在Shape Retrieval Contest(SHREC)2012数据库和SHREC 2013数据库上进行实验。实验证明,该方法的效果较其他传统方法检索准确度更高。
Sketch-based shape retrieval(SBSR)has become a hot research spot in the field of model retrieval,pattern recognition,and computer vision.3D deep representation based on convolutional neural network(CNN)enables significant performance improvement over state-of-the-arts in task of 3D shape retrieval.Motivated by this,in this paper a sketch-based 3D model retrieval algorithm by utilizing entropy representative views and CNN feature matching is proposed.The representative views are obtained by viewpoint entropy.And the representative views are processed by edge detection to get the contour image of 3D model projection.The CNN descriptors extracted as features for representative view of each object.And the method of feature matching is based on CNN descriptors.Our experiments on Shape Retrieval Contest(SHREC)2012 database and SHREC 2013 database demonstrate that our method is better than state-of-the-art approaches.
作者
刘玉杰
宋阳
李宗民
李华
LIU Yujie;SONG Yang;LI Zongmin;LI Hua(College of Computer&Communication Engineering,China University of Petroleum,Qingdao Shandong 266580,China;Key Laboratory of Intelligent Information Processing,Institute of Computing Technology Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100190,China)
出处
《图学学报》
CSCD
北大核心
2018年第4期735-741,共7页
Journal of Graphics
基金
国家自然科学基金项目(61379106
61379082
61227802)
山东省自然科学基金项目(ZR2013FM036
ZR2015FM011)
关键词
三维模型检索
卷积神经网络
代表性视图
信息熵
3D shape retrieval
convolutional neural network
representative view
entropy
