Content-based image retrieval by combining convolutional neural networks and sparse representation

被引:1
作者
Amir Sezavar
Hassan Farsi
Sajad Mohamadzadeh
机构
[1] University of Birjand,Department of Electrical and Computer Engineering
来源
Multimedia Tools and Applications | 2019年 / 78卷
关键词
Content-based image retrieval; Deep learning; Convolutional neural networks; Sparse representation;
D O I
暂无
中图分类号
学科分类号
摘要
As stored data and images on memory disks increase, image retrieval has a necessary task on image processing. Although lots of researches have been reported for this task so far, semantic gap between low level features of images and human concept is still an important challenge on content-based image retrieval. For this task, a robust method is proposed by a combination of convolutional neural network and sparse representation, in which deep features are extracted by using CNN and sparse representation to increase retrieval speed and accuracy. The proposed method has been tested on three common databases on image retrieval, named Corel, ALOI and MPEG7. By computing metrics such as P(0.5), P(1) and ANMRR, experimental results show that the proposed method has achieved higher accuracy and better speed compared to state-of-the-art methods.
引用
收藏
页码:20895 / 20912
页数:17
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