The DOI matches its Crossref record.
Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval
Open accessAbstract
This paper discusses the interest of binary partition trees as a region-oriented image representation. Binary partition trees concentrate in a compact and structured representation a set of meaningful regions that can be extracted from an image. They offer a multiscale representation of the image and define a translation invariant 2-connectivity rule among regions. As shown in this paper, this representation can be used for a large number of processing goals such as filtering, segmentation, information retrieval and visual browsing. Furthermore, the processing of the tree representation leads to very efficient algorithms. Finally, for some applications, it may be interesting to compute the binary partition tree once and to store it for subsequent use for various applications. In this context, the paper shows that the amount of bits necessary to encode a binary partition tree remains moderate.
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.
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
Cited by
(2013) · Liverpool John Moores University · Cited by 133
Multilayer SOM With Tree-Structured Data for Efficient Document Retrieval and Plagiarism Detection
Chow, T. W. S., & Rahman, M. K. M. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2009.2023394 · Cited by 67
Rahman, M. K. M., & Chow, T. W. S. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.09.002 · Cited by 11
Metadata
- Authors
- Salembier, P., & Garrido, L.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2000-04
- Volume
- 9
- Issue
- 4
- Pages
- 561-576
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2146037576
- Cited by
- 587
- References
- 0
- FWCI
- 15.78
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. https://app.cochat.ai/papers/10.1109/83.841934
- The paper
- Salembier, P., & Garrido, L. (2000). Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. IEEE Transactions on Image Processing. https://doi.org/10.1109/83.841934
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
