Demo Data from VLDB 2026 / reviewers in the wild / expert
Young-In Shin
dblp:51/5988
· DBLP profile ↗
4ranked-venue papers
2as first author
0since 2021 · last 2007
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 4 · 2 first-authorArtificial intelligence and machine learning · 2 · 2 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.
| Databases, data mining, and information retrieval
1 paper |
Data mining · 50% Machine learning and data management · 50% | |
| Artificial intelligence
1 paper |
Kernel, tree and ensemble methods · 100% | |
| Computer graphics and multimedia
1 paper |
Multimedia analysis and retrieval · 50% Image and video processing · 50% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Kernel, tree and ensemble methods › kernel function
sequence kernels |
0.1 | 1 | 2007 | Parametric Kernels for Sequence Data Analysis · IJCAI 2007 |
Machine learning and data management
kernel methods |
0.1 | 1 | 2006 | Unsupervised Order-Preserving Regression Kernel for Sequence Analysis · AAAI 2006 |
Data mining
sequence analysis |
0.1 | 1 | 2006 | Unsupervised Order-Preserving Regression Kernel for Sequence Analysis · AAAI 2006 |
Image and video processing
image transform |
0.0 | 1 | 1992 | Spatially invariant image sequences · IEEE Trans. Image Process. 1992 |
Multimedia analysis and retrieval
video analysis |
0.0 | 1 | 1992 | Spatially invariant image sequences · IEEE Trans. Image Process. 1992 |
Methods — techniques the papers use, named apart from their topics
parametric kernels · 0.1unsupervised learning · 0.1linear filtering · 0.0additive image model · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | Parametric Kernels for Sequence Data Analysis
Young-In Shin, Donald S. Fussell |
IJCAI | 1 |
| 2006 | Unsupervised Order-Preserving Regression Kernel for Sequence Analysis
Young-In Shin |
AAAI | 1 |
| 1992 | Compression and reconstruction of spatially invariant linearly additive image sequences with suppression of noise and undesired features
John W. V. Miller, Malayappan Shridhar, James B. Farison, Young-In Shin |
J. Vis. Commun. Image Represent. | 4 |
| 1992 | Spatially invariant image sequencesabstractThe authors define linearly additive spatially invariant image sequences and present an explicit mathematical model for describing them. In such a sequence, all objects are positionally invariant in each image of the sequence but have varying gray-scale contributions to the successive images of the sequence. The various components (features or processes) of the scene or object contribute additively to each image of the sequence, but each component has a characteristic variation (signature) from image to image due to the variation of the function, parameter or spectral band over the sequence. Objects with different spectral characteristics will have different image sequence signatures which can be used to distinguish them. Also presented are the general formulation, derivation, and explicit expression for the linear filter, called the simultaneous diagonalization (SD) filter, that calculates a single new image from the sequence such that a desired process is emphasized and any number of undesired processes is suppressed in the filtered image. John W. V. Miller, James B. Farison, Young-In Shin |
IEEE Trans. Image Process. | 3 |