Local Intensity Order Pattern for feature description
Abstract
This paper presents a novel method for feature description based on intensity order. Specifically, a Local Intensity Order Pattern(LIOP) is proposed to encode the local ordinal information of each pixel and the overall ordinal information is used to divide the local patch into subregions which are used for accumulating the LIOPs respectively. Therefore, both local and overall intensity ordinal information of the local patch are captured by the proposed LIOP descriptor so as to make it a highly discriminative descriptor. It is shown that the proposed descriptor is not only invariant to monotonic intensity changes and image rotation but also robust to many other geometric and photometric transformations such as viewpoint change, image blur and JEPG compression. The proposed descriptor has been evaluated on the standard Oxford dataset and four additional image pairs with complex illumination changes. The experimental results show that the proposed descriptor obtains a significant improvement over the existing state-of-the-art descriptors.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
LIOP and DAISY are highly effective in recognition and classification due to their robustness to numerous variations and distortions.
“Meanwhile, local intensity order pattern (LIOP)36 and DAISY37 have been demonstrated as highly effective methods applied in the recognition and classification task due to their robustness to numerous variations and distortions.”
From Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector (2015) · cites this paper as [36]
The citation lists haven’t been fetched yet
References 25 · Cited by 317
- Authors
- Wang, Z., Fan, B., & Wu, F.
- Affiliations
- Chinese Academy of Sciences (CN)
- ORCID
- Venue
- 2011 International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2011-11
- Pages
- 603-610
- OpenAlex
- W2163096995
- Cited by
- 317
- References
- 25
- Citations by year
- 2025: 5 · 2024: 7 · 2023: 9 · 2022: 14 · 2021: 16 · 2020: 18 · 2019: 23 · 2018: 28 · 2017: 40 · 2016: 40 · 2015: 59 · 2014: 31 · 2013: 25 · 2012: 2
- FWCI
- 13.85
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Funders
- National Natural Science Foundation of China
- 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 › Face and Expression Recognition
- Keywords
- feature description, ordinal information, rotation invariance, illumination invariance, image matching
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