Sketch Based Image Retrieval Based on Multi-layer Semantic Feature and Deep Convolutional Neural Network

被引：0
|
作者：
Liu Y. [1 ]
Yu D. [1 ]
Pang Y. [1 ]
Li Z. [1 ]
Li H. [2 ]
机构：
[1] College of Computer & Communication Engineering, China University of Petroleum, Qingdao
[2] Institute of Computing Technology, Chinese Academy of Sciences, Beijing
来源：
Li, Zongmin (lizongmin@upc.edu.cn) | 2018年 / Institute of Computing Technology卷 / 30期
关键词：
Deep convolutional neural network; Feature fusion; Multi-layer semantic features; Sketch based image retrieval;
D O I：
10.3724/SP.J.1089.2018.16544
中图分类号：
学科分类号：
摘要：
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. © 2018, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
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页码：651 / 657
页数：6
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