Demo Data from EDBT 2026 / reviewers in the wild / expert
Omer Rotem
dblp:94/4368
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorTheory of computation · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Graph algorithms and graph theory · 67% Approximation and online algorithms · 33% | |
| Artificial intelligence
1 paper |
Segmentation and scene understanding · 100% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Approximation and online algorithms › approximation algorithms
approximation algorithms for graph problems |
0.4 | 1 | 2020 | Constant girth approximation for directed graphs in subquadratic time · STOC 2020 |
Graph algorithms and graph theory › graph spanners
roundtrip spanners |
0.4 | 1 | 2020 | Constant girth approximation for directed graphs in subquadratic time · STOC 2020 |
Computer vision › Segmentation and scene understanding
image segmentation |
0.1 | 1 | 2007 | Combining Region and Edge Cues for Image Segmentation in a Probabilistic Gaussian Mixture Framework · CVPR 2007 |
Computer vision › Segmentation and scene understanding › image segmentation
probabilistic segmentation |
0.1 | 1 | 2007 | Combining Region and Edge Cues for Image Segmentation in a Probabilistic Gaussian Mixture Framework · CVPR 2007 |
Image and video processing
image segmentation |
0.0 | 1 | 2007 | Combining Region and Edge Cues for Image Segmentation in a Probabilistic Gaussian Mixture Framework · CVPR 2007 |
Methods — techniques the papers use, named apart from their topics
gaussian mixture model · 0.1expectation-maximization · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Constant girth approximation for directed graphs in subquadratic timeabstractIn this paper we provide a Õ(m√n) time algorithm that computes a 3-multiplicative approximation of the girth of a n-node m-edge directed graph with non-negative edge lengths. This is the first algorithm which approximates the girth of a directed graph up to a constant multiplicative factor faster than All-Pairs Shortest Paths (APSP) time, i.e. O(mn). Additionally, for any integer k ≥ 1, we provide a deterministic algorithm for a O(kloglogn)-multiplicative approximation to the girth in directed graphs in Õ(m 1+1/k ) time. Combining the techniques from these two results gives us an algorithm for a O(klogk)-multiplicative approximation to the girth in directed graphs in Õ(m 1+1/k ) time. Our results naturally also provide algorithms for improved constructions of roundtrip spanners, the analog of spanners in directed graphs. Shiri Chechik, Yang P. Liu, Omer Rotem, Aaron Sidford |
STOC | 3 |
| 2007 | Combining Region and Edge Cues for Image Segmentation in a Probabilistic Gaussian Mixture FrameworkabstractIn this paper we propose a new segmentation algorithm which combines patch-based information with edge cues under a probabilistic framework. We use a mixture of multiple Gaussians for building the statistical model with color and spatial features, and we incorporate edge information based on texture, color and brightness differences into the EM algorithm. We evaluate our results qualitatively and quantitatively on a large data-set of natural images and compare our results to other state-of-the-art methods. Omer Rotem, Hayit Greenspan, Jacob Goldberger |
CVPR | 1 |