摘要
针对手绘图像检索领域中手绘图像的语义特征,为了深度发掘手绘图像的语义特征,并获得高效、准确的检索结果,提出一种基于多层语义特征和深度卷积网络的融合网络的方法.首先提出针对手绘图像语义特征的分层的概念,并构建与多层语义特征相对应的多层深度卷积神经网络来学习不同层次的深度特征,然后通过特征融合,实现多层深度语义特征的融合,形成最终的特征描述子,达到高精度的检索.在基准数据库Flickr15k上的实验结果表明该方法是可行、有效的.
In this paper,we studied the semantic features of the free-hand sketches in the research field of SBIR(sketch based image retrieval),and proposed a new approach to dig out the semantic property in sketches and improve the performance of sketches retrieval,which is based on multi-layer semantic feature learning and deep convolutional neural network.Our methods are demonstrated as follow:firstly,we put forward a new conception of multi-layer semantic feature descriptors;secondly,we constructed a corresponding multiple layers of deep convolutional neural network to learn the deep features of sketches;thirdly,we combined semantic features of different layers by the feature fusion algorithm to forming the final feature representations and to realize the high retrieval accuracy.The experiment on benchmark Flickr15k dataset proves the efficiency and accuracy of our proposed method.
作者
刘玉杰
于邓
庞芸萍
李宗民
李华
Liu Yujie;Yu Deng;Pang Yunping;Li Zongmin;Li Hua(College of Computer&Communication Engineering,China University of Petroleum,Qingdao 266580;Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190)
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2018年第4期651-657,共7页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(61379106)
山东省自然科学基金(ZR2013FM036
ZR2015FM011)
浙江大学CAD&CG重点实验室开放基金(A1315)
关键词
手绘检索
多层语义特征
深度卷积神经网络
特征融合
sketch based image retrieval
multi-layer semantic features
deep convolutional neural network
feature fusion
