SCALABLE IMAGE RETRIEVAL BY MULTILABEL DEEP HASHING
IJRTI1909009
Volume 4, Issue 9
Page 47 - 54
September 2019
205,620 Downloads
PublishedOpen AccessResearch PaperTransparent Peer ReviewedCC BY 4.0
ISSN2456-3315
Impact Factor10.57
UGC CAREApproved
Low Cost₹599 / $45
CrossRefDOI
ESTD2016
Peer ReviewTransparent
AccessOpen
Submission: Always Open
Paper Title
SCALABLE IMAGE RETRIEVAL BY MULTILABEL DEEP HASHING
Authors
Sonal U Darade
Keywords
Scalable image search, fast similarity search, hashing, deep learning, multi-label learning.
Abstract
In computer vision Hashing have more scope and it is used to generate a binary hash value. So, we used Deep Hashing to learn a compact binary code for single and multiple layers of projections. There are many different methods which is used for binary code generation. We used supervised and multilabel supervised learning method for binary code value.
Choose the style your journal or department asks for, then copy it.
Every version below is generated from this paper's own record.
IJRTI — journal style
"SCALABLE IMAGE RETRIEVAL BY MULTILABEL DEEP HASHING", IJRTI - International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.4, Issue 9, page no.47 - 54, September-2019, Available :https://ijrti.org/papers/IJRTI1909009.pdf
APA — 7th edition
Darade, S. U. (2019). SCALABLE IMAGE RETRIEVAL BY MULTILABEL DEEP HASHING. International Journal for Research Trends and Innovation, 4(9), 47 - 54. https://ijrti.org/viewpaperforall.php?paper=IJRTI1909009
MLA — 9th edition
Darade, Sonal U. "SCALABLE IMAGE RETRIEVAL BY MULTILABEL DEEP HASHING." International Journal for Research Trends and Innovation, vol. 4, no. 9, 2019, pp. 47 - 54, https://ijrti.org/viewpaperforall.php?paper=IJRTI1909009.
Chicago — 17th, bibliography
Darade, Sonal U. "SCALABLE IMAGE RETRIEVAL BY MULTILABEL DEEP HASHING." International Journal for Research Trends and Innovation 4, no. 9 (2019): 47 - 54. https://ijrti.org/viewpaperforall.php?paper=IJRTI1909009.
Harvard — author–date
Darade, S.U. (2019) 'SCALABLE IMAGE RETRIEVAL BY MULTILABEL DEEP HASHING', International Journal for Research Trends and Innovation, 4(9), pp. 47 - 54. Available at: https://ijrti.org/viewpaperforall.php?paper=IJRTI1909009
IEEE — numbered reference
S. U. Darade, "SCALABLE IMAGE RETRIEVAL BY MULTILABEL DEEP HASHING," IJRTI, vol. 4, no. 9, pp. 47 - 54, Sep. 2019.
Vancouver — biomedical
Darade SU. SCALABLE IMAGE RETRIEVAL BY MULTILABEL DEEP HASHING. IJRTI. 2019 Sep;4(9):47 - 54.
AMA — 11th edition
Darade SU. SCALABLE IMAGE RETRIEVAL BY MULTILABEL DEEP HASHING. IJRTI. 2019;4(9):47 - 54. Accessed at https://ijrti.org/viewpaperforall.php?paper=IJRTI1909009
BibTeX — Zotero, Mendeley, LaTeX
@article{IJRTI1909009,
author = {Sonal U Darade},
title = {SCALABLE IMAGE RETRIEVAL BY MULTILABEL DEEP HASHING},
journal = {International Journal for Research Trends and Innovation},
volume = {4},
number = {9},
pages = {47 - 54},
year = {2019},
month = {September},
issn = {2456-3315},
url = {https://ijrti.org/viewpaperforall.php?paper=IJRTI1909009}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - Darade, Sonal U
TI - SCALABLE IMAGE RETRIEVAL BY MULTILABEL DEEP HASHING
T2 - International Journal for Research Trends and Innovation
JA - IJRTI
VL - 4
IS - 9
PY - 2019
SN - 2456-3315
UR - https://ijrti.org/viewpaperforall.php?paper=IJRTI1909009
SP - 47
EP - 54
ER -
International Journal for Research Trends and InnovationPublished by IJRTI (JW Publication)
2456-3315ISSN
10.57Impact Factor
2016ESTD Year
OpenAccess
Impact Factor 10.57 calculated by Google Scholar and Semantic Scholar.
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 10.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
UGC CARE Approved Journal — Transparent Peer-reviewed journal aligned with UGC’s 2025 suggestive parameters, with CrossRef DOI registration and Scopus Standard metadata on every published paper.
Disclaimer:
The content, data and findings in this article are based on the authors’ research and have been
peer-reviewed for academic purposes only. Readers are advised to verify all information before practical
or commercial use. The journal and its editorial board are not liable for any errors, losses or
consequences arising from its use.
Declarations
Funding
No external funding was received for this study.
Conflict of Interest
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to thank the reviewers and the editorial board of International Journal for Research Trends and Innovation for their careful reading and constructive comments, and all colleagues who supported the preparation of this manuscript.