The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Similarity Measure of Images using SIFT and ORB Feature Matching
Open accessAbstract
Abstract: In this study, similarities between images are identified and examined. A collection of images made from an image of the same scene or item taken under varied atmospheric conditions, such as different perspectives or lighting settings, and then altered in myriad ways by the very same starting image. The process of determining picture similarity is comparing visual information to evaluate how similar it is. Based on the input, it finds good matches and presents the proportion percent exceptional matches between the photos. It compares the supplied input to calculate the similarity measure. For decades, picture resemblance has been a fundamental problem in the fields of object recognition.When two photos are compared, Image Similarity generates a result that represents how physically similar they are. The measure of comparison of the two photographs is given a numerical number in this test. The similarity score between two photos ranges from 0 to 1 respectively.We have proposed a solution using SIFT & ORB Feature Matching
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Feature-Based Similarity [23-28]: By extracting high-level primitives such as edges, corners, and shapes using algorithms like ORB (Oriented FAST and Rotated BRIEF), these methods are robust to illumination changes and affine transformations.”
From A Survey and Tutorial on Image Quality Assessment with a Contrast-Weighted Structural Similarity Framework (2026) · cites this paper as [23]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Comparison of blockchain platforms: a systematic review and healthcare examples
Kuo, T.-T., Rojas, H. Z., & Lucila Ohno-machado, A. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy185 · Cited by 284
Image Feature Matching and Object Detection Using Brute-Force Matchers
Jakubovic, A., & Velagić, J. (2018) · International Symposium ELMAR (ELMAR) · doi:10.23919/elmar.2018.8534641 · Cited by 124
Analysis of various image feature extraction methods against noisy image: SIFT, SURF and HOG
Routray, S., Ray, A. K., & Mishra, C. (2017) · International Conference on Electrical, Computer and Communication Technologies (ICECCT) · doi:10.1109/icecct.2017.8117846 · Cited by 61
An overview of similarity measures for images
der Weken, D. V., Nachtegael, M., & Kerre, E. E. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2002.5745363 · Cited by 44
FLANN Based Matching with SIFT Descriptors for Drowsy Features Extraction
Vijayan, V., & Pushpalatha, K. P. (2019) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip47207.2019.8985924 · Cited by 43
Image Feature Point Matching Based on Improved SIFT Algorithm
Zhong, B., & Li, Y. (2019) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc47709.2019.8981329 · Cited by 30
SIFTpack: A Compact Representation for Efficient SIFT Matching
Gilinsky, A., & Zelnik‐Manor, L. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.101 · Cited by 16
An overview of similarity measures for images
der Weken, V., Nachtegael, & Kerre (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2002.1004621 · Cited by 13
Feature-point extraction based on an improved SIFT algorithm
Joo, H.-B., & Jeon, J. W. (2017) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas.2017.8204463 · Cited by 9
Image similarity for rotation invariants image retrieval system
Augustine, K. V., & Dong-jun, H. (2009) · doi:10.1109/mmcs.2009.5256716 · Cited by 7
Research works printed but not linked: 2
- Jian Zhao,Yiliu Feng, Shandong Yuan, Wan Zheng Cai, "A more brief and efficient SIFT image matching algorithm for computer vision", in IEEE International Conference.
- Patil J.S., Pradeepini G., "SIFT: A comprehensive", in International Journal of Recent Technology and Engineering,2019.
Cited by
Sorted by citations
Sarid: Arabic Storyteller Using a Fine-Tuned LLM and Text-to-Image Generation
Alabdulrahman, M., Khayyat, R., Almowallad, K., & Alharz, Z. (2024) · International Conference on Computer and Automation Engineering · doi:10.1109/iccae59995.2024.10569190 · Cited by 4
ORB-SIFT Hybrid Feature Extraction: A Unified Feature Extraction Approach
Kumawat, D., Abhayankar, D., & Tanwani, S. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-86290-8_10 · Cited by 1
Agaian, S. S., Grigoryan, A. M., & Ayunts, H. (2026) · Information · doi:10.3390/info17070632
Awad, A., Eldosoky, M. A. A., Soliman, A. M., & Mahmoud, N. M. (2025) · Applied Acoustics · doi:10.1016/j.apacoust.2025.110906
Rule-based generative modeling in ceramic tableware industry applications
Gaspar, F. L., Carreira, D., Rodrigues, N., Miragaia, R., Ribeiro, J., Grilo, C., & Pereira, A. M. D. J. (2025) · Virtual Reality · doi:10.1007/s10055-025-01194-1
Metadata
- Authors
- Singh, S., Siramdas, S., Tanmayi, K., & Koli, L.
- ORCID
- Singh, Shivani
- Venue
- International Journal for Research in Applied Science and Engineering Technology
- Published
- 2022-06-30
- Volume
- 10
- Issue
- 6
- Pages
- 2354-2359
- ISSN
- 2321-9653 (ISSN-L)
- OpenAlex
- W4283371682
- Cited by
- 5
- References
- 10
- Citations by year
- 2025: 4 · 2024: 1
- FWCI
- 0.41
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- International Journal for Research in Applied Science and Engineering Technology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- image similarity, SIFT, ORB, feature matching, object recognition, image matching, similarity scores
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
