Recent developments of content-based image retrieval (CBIR)

被引:84
|
作者
Li, Xiaoqing [1 ,2 ]
Yang, Jiansheng [1 ,2 ]
Ma, Jinwen [1 ,2 ]
机构
[1] Peking Univ, Sch Math Sci, Dept Informat & Computat Sci, Beijing 100871, Peoples R China
[2] Peking Univ, LMAM, Beijing 100871, Peoples R China
关键词
Content-based image retrieval; Image representation; Database search; Computer vision; Big data; Deep learning; PRODUCT QUANTIZATION; REPRESENTATION; NETWORK;
D O I
10.1016/j.neucom.2020.07.139
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the development of Internet technology and the popularity of digital devices, Content-Based Image Retrieval (CBIR) has been quickly developed and applied in various fields related to computer vision and artificial intelligence. Currently, it is possible to retrieve related images effectively and efficiently from a large scale database with an input image. In the past ten years, great efforts have been made for new theories and models of CBIR and many effective CBIR algorithms have been established. In this paper, we present a survey on the fast developments and applications of CBIR theories and algorithms during the period from 2009 to 2019. We mainly review the technological developments from the viewpoint of image representation and database search. We further summarize the practical applications of CBIR in the fields of fashion image retrieval, person re-identification, e-commerce product retrieval, remote sensing image retrieval and trademark image retrieval. Finally, we discuss the future research directions of CBIR with the challenge of big data and the utilization of deep learning techniques.& nbsp; (c) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页码:675 / 689
页数:15
相关论文
共 50 条
  • [31] Faceted content-based image retrieval
    Amato, Giuseppe
    Meghini, Carlo
    DEXA 2008: 19TH INTERNATIONAL CONFERENCE ON DATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS, 2008, : 402 - 406
  • [32] Content-based image retrieval with WISFC
    Zhang, H. (guwenjiao1989@126.com), 1600, Binary Information Press (10):
  • [33] Prefetching for content-based image retrieval
    Yoon, J
    Jayant, N
    IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOL I AND II, PROCEEDINGS, 2002, : A413 - A416
  • [34] Content-based ultrasound image retrieval
    Kwak, DM
    Kim, BS
    Park, CH
    Kim, SJ
    Kim, YM
    Park, KH
    METMBS'01: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MATHEMATICS AND ENGINEERING TECHNIQUES IN MEDICINE AND BIOLOGICAL SCIENCES, 2001, : 512 - 517
  • [35] Content-based image retrieval - A survey
    Choras, Ryszard S.
    BIOMETRICS, COMPUTER SECURITY SYSTEMS AND ARTIFICIAL INTELLIGENCE APPLICATIONS, 2006, : 31 - 44
  • [36] Content-Based Histopathological Image Retrieval
    Nunez-Fernandez, Camilo
    Farias, Humberto
    Solar, Mauricio
    SENSORS, 2025, 25 (05)
  • [37] Study on Content-Based of Image Retrieval
    Zhang, Chi
    Huang, Lei
    LISS 2013, 2015, : 591 - 594
  • [38] Content-based retinal image retrieval
    Sukhia, Komal Nain
    Riaz, Muhammad Mohsin
    Ghafoor, Abdul
    IET IMAGE PROCESSING, 2019, 13 (09) : 1525 - 1534
  • [39] Localized content-based image retrieval
    Rahmani, Rouhollah
    Goldman, Sally A.
    Zhang, Hui
    Cholleti, Sharath R.
    Fritts, Jason E.
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2008, 30 (11) : 1902 - 1912
  • [40] Content-based image retrieval speedup
    Fadaei, Sadegh
    Rashno, Abdolreza
    Rashno, Elyas
    2019 5TH IRANIAN CONFERENCE ON SIGNAL PROCESSING AND INTELLIGENT SYSTEMS (ICSPIS 2019), 2019,