Demo Data from SIGIR 2026 / reviewers in the wild / expert
Premysl Cech
dblp:117/3799
· DBLP profile ↗
12ranked-venue papers
3as first author
0since 2021 · last 2020
0000-0001-6291-5909ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 8 · 3 first-authorGraphics, computer vision, multimedia, augmented reality and games · 4Artificial intelligence and machine learning · 2 · 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.
| Computer graphics and multimedia
1 paper |
Multimedia analysis and retrieval · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Multimedia analysis and retrieval › video retrieval
interactive video search |
0.4 | 1 | 2019 | A Framework for Effective Known-item Search in Video · ACM Multimedia 2019 |
Multimedia analysis and retrieval › video retrieval
known-item search |
0.4 | 1 | 2019 | A Framework for Effective Known-item Search in Video · ACM Multimedia 2019 |
Multimedia analysis and retrieval
video retrieval |
0.4 | 1 | 2019 | A Framework for Effective Known-item Search in Video · ACM Multimedia 2019 |
Methods — techniques the papers use, named apart from their topics
video analysis · 0.4query log analysis · 0.4interactive search · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Pivot-based approximate k-NN similarity joins for big high-dimensional data
Premysl Cech, Jakub Lokoc, Yasin N. Silva |
Inf. Syst. | 1 |
| 2019 | VIRET: A Video Retrieval Tool for Interactive Known-item SearchabstractKnown-item search in large video collections still represents a challenging task for current video retrieval systems that have to rely both on state-of-the-art ranking models and interactive means of retrieval. We present a general overview of the current version of the VIRET tool, an interactive video retrieval system that successfully participated at several international evaluation campaigns. The system is based on multi-modal search and convenient inspection of results. Based on collected query logs of four users controlling instances of the tool at the Video Browser Showdown 2019, we highlight query modification statistics and a list of successful query formulation strategies. We conclude that the VIRET tool represents a competitive reference interactive system for effective known-item search in one thousand hours of video. Jakub Lokoc, Gregor Kovalcík, Tomás Soucek, Jaroslav Moravec, Premysl Cech |
ICMR | 5 |
| 2019 | A Framework for Effective Known-item Search in VideoabstractSearching for one particular scene in a large video collection (known-item search) represents a challenging task for video retrieval systems. According to the recent results reached at evaluation campaigns, even respected approaches based on machine learning do not help to solve the task easily in many cases. Hence, in addition to effective automatic multimedia annotation and embedding, interactive search is recommended as well. This paper presents a comprehensive description of an interactive video retrieval framework VIRET that successfully participated at several recent evaluation campaigns. Utilized video analysis, feature extraction and retrieval models are detailed as well as several experiments evaluating effectiveness of selected system components. The results of the prototype at the Video Browser Showdown 2019 are highlighted in connection with an analysis of collected query logs. We conclude that the framework comprise a set of effective and efficient models for most of the evaluated known-item search tasks in 1000 hours of video and could serve as a baseline reference approach. The analysis also reveals that the result presentation interface needs improvements for better performance of future VIRET prototypes. Jakub Lokoc, Gregor Kovalcík, Tomás Soucek, Jaroslav Moravec, Premysl Cech |
ACM Multimedia | 5 |
| 2019 | VIRET Tool Meets NasNet
Jakub Lokoc, Gregor Kovalcík, Tomás Soucek, Jaroslav Moravec, Jan Bodnár, Premysl Cech |
MMM (2) | 6 |
| 2018 | Learning communication patterns for malware discovery in HTTPs data
Jan Kohout, Tomás Komárek, Premysl Cech, Jan Bodnár, Jakub Lokoc |
Expert Syst. Appl. | 3 |
| 2017 | Comparing MapReduce-Based k-NN Similarity Joins on Hadoop for High-Dimensional Data
Premysl Cech, Jakub Marousek, Jakub Lokoc, Yasin N. Silva, Jeremy Starks |
ADMA | 1 |
| 2017 | Malware Discovery Using Behaviour-Based Exploration of Network Traffic
Jakub Lokoc, Tomás Grosup, Premysl Cech, Tomás Pevný, Tomás Skopal |
SISAP | 3 |
| 2016 | Feature Extraction and Malware Detection on Large HTTPS Data Using MapReduce
Premysl Cech, Jan Kohout, Jakub Lokoc, Tomás Komárek, Jakub Marousek, Tomás Pevný |
SISAP | 1 |
| 2015 | A Web Portal for Effective Multi-model Exploration
Tomás Grosup, Premysl Cech, Jakub Lokoc, Tomás Skopal |
MMM (2) | 2 |
| 2015 | Evaluating Multilayer Multimedia Exploration
Juraj Mosko, Jakub Lokoc, Tomás Grosup, Premysl Cech, Tomás Skopal, Jan Lansky |
SISAP | 4 |
| 2014 | On indexing metric spaces using cut-regions
Jakub Lokoc, Juraj Mosko, Premysl Cech, Tomás Skopal |
Inf. Syst. | 3 |
| 2012 | Cut-Region: A Compact Building Block for Hierarchical Metric Indexing
Jakub Lokoc, Premysl Cech, Tomás Skopal |
SISAP | 2 |