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Omer Rotem

dblp:94/4368 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Approximation and online algorithms › approximation algorithms
approximation algorithms for graph problems
0.412020
Constant girth approximation for directed graphs in subquadratic time · STOC 2020
Graph algorithms and graph theory › graph spanners
roundtrip spanners
0.412020
Constant girth approximation for directed graphs in subquadratic time · STOC 2020
Computer vision › Segmentation and scene understanding
image segmentation
0.112007
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
0.112007
Combining Region and Edge Cues for Image Segmentation in a Probabilistic Gaussian Mixture Framework · CVPR 2007
Image and video processing
image segmentation
0.012007
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
YearPublicationVenuePosition
2020 Constant girth approximation for directed graphs in subquadratic time
abstract
In 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
STOC3
2007 Combining Region and Edge Cues for Image Segmentation in a Probabilistic Gaussian Mixture Framework
abstract
In 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
CVPR1