文章基本信息

标题：Performance Evaluation of SIFT and Convolutional Neural Network for Image Retrieval
本地全文：下载
作者：Varsha Devi Sachdeva ; Junaid Baber ; Maheen Bakhtyar 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2017
卷号：8
期号：12
DOI：10.14569/IJACSA.2017.081268
出版社：Science and Information Society (SAI)
摘要：Convolutional Neural Network (NN) has gained a lot of attention of the researchers due to its high accuracy in classification and feature learning. In this paper, we evaluated the performance of CNN used as feature for image retrieval with the gold standard feature, aka SIFT. Experiments are conducted on famous Oxford 5k data-set. The mAP of SIFT and CNN is 0.6279 and 0.5284, respectively. The performance of CNN is also compared with bag of visual word (BoVW) model. CNN achieves better accuracy than BoVW.
关键词：Computer vision; SIFT; CNN; image retrieval; precision; recall