An Image Retrieval Algorithm Based on Multiple Convolutional Features of RPN and Weighted Cosine Similarity

被引：0

作者：

Liu, Xinhua ^{[1
,2
]}

Hu, Gaoqiang ^{[1
,2
]}

Ma, Xiaolin ^{[1
,2
]}

Kuang, Hailan ^{[1
,2
]}

机构：

[1] Wuhan Univ Technol, Sch Informat Engn, Wuhan, Peoples R China

[2] Wuhan Univ Technol, Key Lab Fiber Opt Sensing Technol & Informat Proc, Minist Educ, Wuhan, Peoples R China

来源：

PROCEEDINGS OF THE 30TH CHINESE CONTROL AND DECISION CONFERENCE (2018 CCDC) | 2018年

关键词：

Feature Extraction; Deep Learning; Weighted Cosine Similarity; Image Retrieval;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Aiming at solving the difficulty of feature selecting and the poor retrieval result in the image retrieval, we proposed an image retrieval algorithm which was based on multiple convolutional features of RPN and weighted cosine similarity. We used the deep learning network RPN to extract the multiple convolutional features; and ranked the images by the weighted cosine similarity we proposed. To validate this algorithm; we used Oxford Buildings 5k and Paris Buildings 6k as two image retrieval datasets. As a result, our algorithm improved the mAP by 3% similar to 6% and had better retrieval and ranking results.

引用

页码：4095 / 4098

页数：4

共 50 条

[1] Fusion of Deep Features and Weighted VLAD Vectors based on Multiple Features for Image Retrieval
Wang, Yanhong
Cen, Yigang
Liang, Liequan
Zhang, Linna
Voronin, Viacheslav
Mladenovic, Vladimir
XIII INTERNATIONAL SCIENTIFIC-TECHNICAL CONFERENCE DYNAMIC OF TECHNICAL SYSTEMS (DTS-2017), 2017, 132
[2] A Weighted Algorithm Based on Physical Distance and Cosine Similarity for Indoor Localization
Han, Xiaoyu
Yang, Gang
Qu, Shengli
Zhang, Ge
Chi, Minghong
PROCEEDINGS OF THE 2019 14TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2019), 2019, : 179 - 183
[3] Multiple Feature Similarity Based for Image Retrieval
Zhang, Gengning
Zhang, Yafei
Wang, Jiabao
Li, Yang
Li, Hang
Miao, Zhuang
2015 INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS & SIGNAL PROCESSING (WCSP), 2015,
[4] A NEW TRADEMARK IMAGE RETRIEVAL ALGORITHM BASED ON MULTIPLE FEATURES FUSION
Li, Yuhua
Han, Xu
Liang, Hui
Li, Pu
Shi, Xiayang
Chang, Jian
JOURNAL OF NONLINEAR AND CONVEX ANALYSIS, 2021, 22 (09) : 1831 - 1847
[5] IMAGE RETRIEVAL BASED ON MULTIPLE FEATURES
Su, Ching-Hung
Abd Wahab, Mohd Helmy
Chiu, Huang-Sen
Hsieh, Tsai-Ming
4TH INTERNATIONAL CONFERENCE ON SOFTWARE TECHNOLOGY AND ENGINEERING (ICSTE 2012), 2012, : 343 - +
[6] Image instance retrieval based on deep convolutional features
Li Z.-D.
Zhong Y.
Tao P.
Chen M.
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition), 2019, 49 (01): : 275 - 282
[7] Image Retrieval Algorithm Based on Convolutional Neural Network
Liu, Hailong
Li, Baoan
Lv, Xueqiang
Huang, Yue
PROCEEDINGS OF THE 2016 2ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INDUSTRIAL ENGINEERING (AIIE 2016), 2016, 133 : 278 - 281
[8] Image Retrieval Algorithm based on Convolutional Neural Network
Huang, Wen-qing
Wu, Qiang
CURRENT TRENDS IN COMPUTER SCIENCE AND MECHANICAL AUTOMATION, VOL 1, 2017, : 304 - 314
[9] Relevance feedback techniques and genetic algorithm for image retrieval based on multiple features
Fu, Qi-ming
Liu, Quan
Wang, Xiao-yan
Zhang, Le
INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL, 2011, 14 (04) : 279 - 285
[10] An Effective Similarity Measure Via Genetic Algorithm for Content Based Image Retrieval with Extensive Features
Syam, Baddeti
Rao, Yarravarapu
INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY, 2013, 10 (02) : 143 - 151

← 1 2 3 4 5 →