Demo Data from VLDB 2026 / reviewers in the wild / expert
Jacopo Iacovacci
dblp:149/2230
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2020
0000-0002-1430-5062ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author
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.
| Artificial intelligence
1 paper |
Motion planning and robot control · 100% | |
| Computer graphics and multimedia
1 paper |
Multimedia analysis and retrieval · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control › path planning
visibility graph |
0.4 | 1 | 2020 | Visibility Graphs for Image Processing · IEEE Trans. Pattern Anal. Mach. Intell. 2020 |
Multimedia analysis and retrieval
image classification |
0.4 | 1 | 2020 | Visibility Graphs for Image Processing · IEEE Trans. Pattern Anal. Mach. Intell. 2020 |
Methods — techniques the papers use, named apart from their topics
image visibility graphs · 0.9graph feature extraction · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Visibility Graphs for Image ProcessingabstractThe family of image visibility graphs (IVG/IHVGs) have been recently introduced as simple algorithms by which scalar fields can be mapped into graphs. Here we explore the usefulness of such\an operator in the scenario of image processing and image classification. We demonstrate that the link architecture of the image visibility graphs encapsulates relevant information on the structure of the images and we explore their potential as image filters. We introduce several graph features, including the novel concept of Visibility Patches, and show through several examples that these features are highly informative, computationally efficient and universally applicable for general pattern recognition and image classification tasks. Jacopo Iacovacci, Lucas Lacasa |
IEEE Trans. Pattern Anal. Mach. Intell. | 1 |