Stop the war!Остановите войну!
for scientists:
Google
Google Scholar
Semantic Scholar
IA Scholar
CiteSeerX
ORCIDThomas Seidl 0001
Person information
- affiliation (since 2016): Ludwig Maximilians University of Munich, Institute for Computer Science, Germany
- affiliation (2002-2016): RWTH Aachen University, Germany
Other persons with the same name
- Thomas Seidl 0002 — Fraunhofer Institute for Factory Operation and Automation (IFF)
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2021
[j45]Daniyal Kazempour
, Johannes Winter, Peer Kröger, Thomas Seidl:
On Methods and Measures for the Inspection of Arbitrarily Oriented Subspace Clusters. Datenbank-Spektrum 21(3): 213-223 (2021)- [c289]Michael Fromm, Evgeniy Faerman, Max Berrendorf, Siddharth Bhargava, Ruoxia Qi, Yao Zhang, Lukas Dennert, Sophia Selle, Yang Mao, Thomas Seidl:
Argument Mining Driven Analysis of Peer-Reviews. AAAI 2021: 4758-4766 - [c288]Sandra Obermeier, Anna Beer, Florian Wahl, Thomas Seidl:
Cluster Flow - an Advanced Concept for Ensemble-Enabling, Interactive Clustering. BTW 2021: 175-194 - [c287]Daniyal Kazempour, Anna Beer, Melanie Oelker, Peer Kröger, Thomas Seidl:
Compound Segmentation via Clustering on Mol2Vec-based Embeddings. e-Science 2021: 60-69 - [c286]Michael Fromm, Max Berrendorf, Sandra Obermeier, Thomas Seidl, Evgeniy Faerman:
Diversity Aware Relevance Learning for Argument Search. ECIR (2) 2021: 264-271 - [c285]Anna Beer, Ekaterina Allerborn, Valentin Hartmann, Thomas Seidl:
KISS - A fast kNN-based Importance Score for Subspaces. EDBT 2021: 391-396 - [c284]Anna Wimbauer, Florian Richter, Thomas Seidl:
PErrCas: Process Error Cascade Mining in Trace Streams. ICPM Workshops 2021: 224-236 - [c283]Anna Beer, Lisa Stephan, Thomas Seidl:
LUCKe - Connecting Clustering and Correlation Clustering. ICDM (Workshops) 2021: 431-440 - [c282]Julian Busch, Maximilian Hünemörder, Janis Held, Peer Kröger, Thomas Seidl:
Implicit Hough Transform Neural Networks for Subspace Clustering. ICDM (Workshops) 2021: 441-448 - [c281]Ludwig Zellner, Janina Sontheim, Florian Richter, Gabriel Lindner, Thomas Seidl:
SCORER-Gap: Sequentially Correlated Rules for Event Recommendation Considering Gap Size. ICDM (Workshops) 2021: 925-934 - [c280]Andreas Lohrer, Anna Beer, Maximilian Archimedes Xaver Hünemörder, Jenny Lauterbach, Thomas Seidl, Peer Kröger:
AnyCORE - An Anytime Algorithm for Cluster Outlier REmoval. LWDA 2021: 145-156 - [c279]Julian Busch, Anton Kocheturov, Volker Tresp, Thomas Seidl:
NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification. SSDBM 2021: 121-132 - [e9]Thomas Seidl, Michael Fromm, Sandra Obermeier:
Proceedings of the LWDA 2021 Workshops: FGWM, KDML, FGWI-BIA, and FGIR, Online, September 1-3, 2021. CEUR Workshop Proceedings 2993, CEUR-WS.org 2021 [contents] - [i15]Julian Busch, Anton Kocheturov, Volker Tresp, Thomas Seidl:
NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification. CoRR abs/2103.03939 (2021) - [i14]Yao Zhang, Yunpu Ma, Thomas Seidl, Volker Tresp:
Adaptive Multi-Resolution Attention with Linear Complexity. CoRR abs/2108.04962 (2021) - [i13]Nataliia Kees, Michael Fromm, Evgeniy Faerman, Thomas Seidl:
Active Learning for Argument Strength Estimation. CoRR abs/2109.11319 (2021) - 2020
- [j44]Florian Richter, Yifeng Lu, Daniyal Kazempour, Thomas Seidl:
"Show Me the Crowds!" Revealing Cluster Structures Through AMTICS. Data Sci. Eng. 5(4): 360-374 (2020) - [j43]Dietrich Trautmann, Michael Fromm, Volker Tresp, Thomas Seidl, Hinrich Schütze:
Relational and Fine-Grained Argument Mining. Datenbank-Spektrum 20(2): 99-105 (2020) - [c278]Florian Richter, Janina Sontheim, Ludwig Zellner, Thomas Seidl:
TADE: Stochastic Conformance Checking Using Temporal Activity Density Estimation. BPM 2020: 220-236 - [c277]Valentyn Melnychuk, Evgeniy Faerman, Ilja Manakov, Thomas Seidl:
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels. CIKM (Workshops) 2020 - [c276]Florian Richter, Yifeng Lu, Daniyal Kazempour, Thomas Seidl:
AMTICS: Aligning Micro-clusters to Identify Cluster Structures. DASFAA (1) 2020: 752-768 - [c275]Julian Busch, Jiaxing Pi, Thomas Seidl:
PushNet: Efficient and Adaptive Neural Message Passing. ECAI 2020: 1039-1046 - [c274]Max Berrendorf, Evgeniy Faerman, Valentyn Melnychuk, Volker Tresp, Thomas Seidl:
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned. ECIR (2) 2020: 3-11 - [c273]Daniyal Kazempour, Peer Kröger, Thomas Seidl:
Towards an Internal Evaluation Measure for Arbitrarily Oriented Subspace Clustering. ICDM (Workshops) 2020: 300-307 - [c272]Daniyal Kazempour, Long Mathias Yan, Peer Kröger, Thomas Seidl:
You see a set of wagons - I see one train: Towards a unified view of local and global arbitrarily oriented subspace clusters. ICDM (Workshops) 2020: 308-315 - [c271]Daniyal Kazempour, Anna Beer, Peer Kröger, Thomas Seidl:
I fold you so! An internal evaluation measure for arbitrary oriented subspace clustering. ICDM (Workshops) 2020: 316-323 - [c270]Thomas Seidl:
Keynote Data Mining on Process Data. ICPM 2020: 1 - [c269]Florian Richter, Yifeng Lu, Ludwig Zellner, Janina Sontheim, Thomas Seidl:
TOAD: Trace Ordering for Anomaly Detection. ICPM 2020: 169-176 - [c268]Ludwig Zellner, Florian Richter, Janina Sontheim, Andrea Maldonado, Thomas Seidl:
Concept Drift Detection on Streaming Data with Dynamic Outlier Aggregation. ICPM Workshops 2020: 206-217 - [c267]Florian Richter, Andrea Maldonado, Ludwig Zellner, Thomas Seidl:
OTOSO: Online Trace Ordering for Structural Overviews. ICPM Workshops 2020: 218-229 - [c266]Andrea Maldonado, Janina Sontheim, Florian Richter, Thomas Seidl:
Performance Skyline: Inferring Process Performance Models from Interval Events. ICPM Workshops 2020: 230-242 - [c265]Yao Zhang, Yifeng Lu, Thomas Seidl:
KNNAC: An Efficient k Nearest Neighbor Based Clustering with Active Core Detection. iiWAS 2020: 62-71 - [c264]Yifeng Lu, Yao Zhang, Florian Richter, Thomas Seidl:
k-Nearest Neighbor based Clustering with Shape Alternation Adaptivity. IJCNN 2020: 1-8 - [c263]Anna Beer, Daniyal Kazempour, Julian Busch, Alexander Tekles, Thomas Seidl:
Grace - Limiting the Number of Grid Cells for Clustering High-Dimensional Data. LWDA 2020: 11-22 - [c262]Felix Borutta, Daniyal Kazempour, Felix Mathy, Peer Kröger, Thomas Seidl:
Detecting Arbitrarily Oriented Subspace Clusters in Data Streams Using Hough Transform. PAKDD (1) 2020: 356-368 - [c261]Anna Beer, Dominik Seeholzer, Nadine Sarah Schüler, Thomas Seidl:
Angle-Based Clustering. SISAP 2020: 312-320 - [c260]Anna Beer, Valentin Hartmann, Thomas Seidl:
Orderings of Data - More Than a Tripping Hazard: Visionary. SSDBM 2020: 17:1-17:4 - [p5]Yifeng Lu, Florian Richter, Thomas Seidl:
Efficient Infrequent Pattern Mining Using Negative Itemset Tree. Complex Pattern Mining 2020: 1-16 - [i12]Julian Busch, Jiaxing Pi, Thomas Seidl:
PushNet: Efficient and Adaptive Neural Message Passing. CoRR abs/2003.02228 (2020) - [i11]Julian Busch, Evgeniy Faerman, Matthias Schubert, Thomas Seidl:
Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering. CoRR abs/2009.12875 (2020) - [i10]Valentyn Melnychuk, Evgeniy Faerman, Ilja Manakov, Thomas Seidl:
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels. CoRR abs/2010.12316 (2020) - [i9]Michael Fromm, Max Berrendorf, Sandra Obermeier, Thomas Seidl, Evgeniy Faerman:
Diversity Aware Relevance Learning for Argument Search. CoRR abs/2011.02177 (2020) - [i8]Michael Fromm, Evgeniy Faerman, Max Berrendorf, Siddharth Bhargava, Ruoxia Qi, Yao Zhang, Lukas Dennert, Sophia Selle, Yang Mao, Thomas Seidl:
Argument Mining Driven Analysis of Peer-Reviews. CoRR abs/2012.07743 (2020)
2010 – 2019
- 2019
- [j42]Janis Held, Anna Beer, Thomas Seidl:
Chain-detection Between Clusters. Datenbank-Spektrum 19(3): 219-230 (2019) - [j41]Daniyal Kazempour, Markus Mauder, Peer Kröger, Thomas Seidl:
Detecting global hyperparaboloid correlated clusters: a Hough-transform based multicore algorithm. Distributed Parallel Databases 37(1): 39-72 (2019) - [j40]Marwan Hassani, Daniel Töws, Alfredo Cuzzocrea, Thomas Seidl:
BFSPMiner: an effective and efficient batch-free algorithm for mining sequential patterns over data streams. Int. J. Data Sci. Anal. 8(3): 223-239 (2019) - [j39]Florian Richter, Thomas Seidl:
Looking into the TESSERACT: Time-drifts in event streams using series of evolving rolling averages of completion times. Inf. Syst. 84: 265-282 (2019) - [c259]Florian Richter, Ludwig Zellner, Imen Azaiz, David Winkel, Thomas Seidl:
LIProMa: Label-Independent Process Matching. Business Process Management Workshops 2019: 186-198 - [c258]Janis Held, Anna Beer, Thomas Seidl:
Chain-detection for DBSCAN. BTW (Workshops) 2019: 173-183 - [c257]Daniyal Kazempour, Maksim Kazakov, Peer Kröger, Thomas Seidl:
DICE: Density-based Interactive Clustering and Exploration. BTW 2019: 547-550 - [c256]Anna Beer, Daniyal Kazempour, Thomas Seidl:
Rock - Let the points roam to their clusters themselves. EDBT 2019: 630-633 - [c255]Daniyal Kazempour, Lisa Krombholz, Peer Kröger, Thomas Seidl:
A Galaxy of Correlations. EDBT 2019: 702-705 - [c254]Daniyal Kazempour, Thomas Seidl:
Insights into a running clockwork: On interactive process-aware clustering. EDBT 2019: 706-709 - [c253]Anna Beer, Daniyal Kazempour, Marcel Baur, Thomas Seidl:
Human Learning in Data Science. HCI (34) 2019: 170-176 - [c252]Daniyal Kazempour, Anna Beer, Thomas Seidl:
Data on RAILs: On Interactive Generation of Artificial Linear Correlated Data. HCI (34) 2019: 184-189 - [c251]Anna Beer, Nadine Sarah Schüler, Thomas Seidl:
A Generator for Subspace Clusters. LWDA 2019: 69-73 - [c250]Maximilian Archimedes Xaver Hünemörder, Anna Beer, Daniyal Kazempour, Thomas Seidl:
CODEC - Detecting Linear Correlations in Dense Clusters using coMAD-based PCA. LWDA 2019: 111-114 - [c249]Daniyal Kazempour, Anna Beer, Oliver Schrüfer, Thomas Seidl:
Clustering Trend Data Time-Series through Segmentation of FFT-decomposed Signal Constituents. LWDA 2019: 127-138 - [c248]Daniyal Kazempour, Long Mathias Yan, Thomas Seidl:
From Covariance to Comode in context of Principal Component Analysis. LWDA 2019: 139-143 - [c247]Florian Richter, Florian Wahl, Alona Sydorova, Thomas Seidl:
k-process: Model-Conformance-based Clustering of Process Instances. LWDA 2019: 161-172 - [c246]Janina Sontheim, Florian Richter, Thomas Seidl:
Temporal Deviations on Event Sequences. LWDA 2019: 173-177 - [c245]Florian Richter, Ludwig Zellner, Janina Sontheim, Thomas Seidl:
Model-Aware Clustering of Non-conforming Traces. OTM Conferences 2019: 193-200 - [c244]Anna Beer, Jennifer Lauterbach, Thomas Seidl:
MORe++: k-Means Based Outlier Removal on High-Dimensional Data. SISAP 2019: 188-202 - [c243]Maximilian Archimedes Xaver Hünemörder, Daniyal Kazempour, Peer Kröger, Thomas Seidl:
SIDEKICK: Linear Correlation Clustering with Supervised Background Knowledge. SISAP 2019: 221-230 - [c242]Daniyal Kazempour, M. A. X. Hünemörder, Thomas Seidl:
On coMADs and Principal Component Analysis. SISAP 2019: 273-280 - [c241]Anna Beer, Daniyal Kazempour, Lisa Stephan, Thomas Seidl:
LUCK- Linear Correlation Clustering Using Cluster Algorithms and a kNN based Distance Function. SSDBM 2019: 181-184 - [c240]Anna Beer, Thomas Seidl:
Graph Ordering and Clustering: A Circular Approach. SSDBM 2019: 185-188 - [c239]Daniyal Kazempour, Kilian Emmerig, Peer Kröger, Thomas Seidl:
Detecting Global Periodic Correlated Clusters in Event Series based on Parameter Space Transform. SSDBM 2019: 222-225 - [c238]Daniyal Kazempour, Thomas Seidl:
On systematic hyperparameter analysis through the example of subspace clustering. SSDBM 2019: 226-229 - [c237]Michael Fromm, Evgeniy Faerman, Thomas Seidl:
TACAM: Topic And Context Aware Argument Mining. WI 2019: 99-106 - [c236]Yifeng Lu, Florian Richter, Thomas Seidl:
LSCMiner: Efficient Low Support Closed Itemsets Mining. WISE 2019: 293-309 - [i7]Michael Fromm, Evgeniy Faerman, Thomas Seidl:
TACAM: Topic And Context Aware Argument Mining. CoRR abs/1906.00923 (2019) - [i6]Max Berrendorf, Evgeniy Faerman, Valentyn Melnychuk, Volker Tresp, Thomas Seidl:
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned. CoRR abs/1911.08342 (2019) - 2018
- [j38]Göran Kauermann, Thomas Seidl:
Data Science: a proposal for a curriculum. Int. J. Data Sci. Anal. 6(3): 195-199 (2018) - [c235]Yifeng Lu, Florian Richter, Thomas Seidl:
Efficient Infrequent Itemset Mining Using Depth-First and Top-Down Lattice Traversal. DASFAA (1) 2018: 908-915 - [c234]Yifeng Lu, Thomas Seidl:
Towards Efficient Closed Infrequent Itemset Mining Using Bi-Directional Traversing. DSAA 2018: 140-149 - [c233]Daniyal Kazempour, Anna Beer, Friederike Herzog, Daniel Kaltenthaler, Johannes-Y. Lohrer, Thomas Seidl:
FATBIRD: A Tool for Flight and Trajectories Analyses of Birds. eScience 2018: 75-82 - [c232]Daniyal Kazempour, Andrian Mörtlbauer, Peer Kröger, Thomas Seidl:
Mirror Mirror on the Wall, What is the Fairest Linear Parameter Space Representation of All? On Representations of Linear Parameter Space in Context of Clustering. LWDA 2018: 169-173 - [c231]Daniyal Kazempour, Thomas Seidl:
Identifying Entangled Data Points on Iteration Trajectories of Clusterings. LWDA 2018: 174-178 - [c230]Daniyal Kazempour, Kevin Bein, Peer Kröger, Thomas Seidl:
D-MASC: A Novel Search Strategy for Detecting Regions of Interest in Linear Parameter Space. SISAP 2018: 163-176 - [c229]Daniyal Kazempour, Anna Beer, Johannes-Y. Lohrer, Daniel Kaltenthaler, Thomas Seidl:
PARADISO: an interactive approach of parameter selection for the mean shift algorithm. SSDBM 2018: 26:1-26:4 - [r2]Thomas Seidl:
Nearest Neighbor Classification. Encyclopedia of Database Systems (2nd ed.) 2018 - 2017
- [j37]Daniel Schüller, Christian Beecks, Marwan Hassani, Jennifer Hinnell, Bela Brenger, Thomas Seidl, Irene Mittelberg:
Automated Pattern Analysis in Gesture Research: Similarity Measuring in 3D Motion Capture Models of Communicative Action. Digit. Humanit. Q. 11(2) (2017) - [j36]Brigitte Boden, Stephan Günnemann, Holger Hoffmann, Thomas Seidl:
MiMAG: mining coherent subgraphs in multi-layer graphs with edge labels. Knowl. Inf. Syst. 50(2): 417-446 (2017) - [j35]Marwan Hassani, Thomas Seidl:
Using internal evaluation measures to validate the quality of diverse stream clustering algorithms. Vietnam. J. Comput. Sci. 4(3): 171-183 (2017) - [c228]Florian Richter, Thomas Seidl:
TESSERACT: Time-Drifts in Event Streams Using Series of Evolving Rolling Averages of Completion Times. BPM 2017: 289-305 - [c227]Thomas Seidl:
Multimedia Similarity Search. BTW (Workshops) 2017: 397 - [c226]Marwan Hassani, Daniel Töws, Thomas Seidl:
Understanding the bigger picture: batch-free exploration of streaming sequential patterns with accurate prediction. SAC 2017: 866-869 - [c225]Merih Seran Uysal, Kai Driessen, Tobias Brockhoff, Thomas Seidl:
Fast Similarity Search with the Earth Mover's Distance via Feasible Initialization and Pruning. SISAP 2017: 141-155 - [c224]Yifeng Lu, Marwan Hassani, Thomas Seidl:
Incremental Temporal Pattern Mining Using Efficient Batch-Free Stream Clustering. SSDBM 2017: 7:1-7:12 - [c223]Daniyal Kazempour, Markus Mauder, Peer Kröger, Thomas Seidl:
Detecting Global Hyperparaboloid Correlated Clusters Based on Hough Transform. SSDBM 2017: 31:1-31:6 - [e8]Christian Beecks, Felix Borutta, Peer Kröger, Thomas Seidl:
Similarity Search and Applications - 10th International Conference, SISAP 2017, Munich, Germany, October 4-6, 2017, Proceedings. Lecture Notes in Computer Science 10609, Springer 2017, ISBN 978-3-319-68473-4 [contents] - 2016
- [j34]Christian Beecks, Marwan Hassani, Bela Brenger, Jennifer Hinnell, Daniel Schüller, Irene Mittelberg, Thomas Seidl:
Efficient Query Processing in 3D Motion Capture Gesture Databases. Int. J. Semantic Comput. 10(1): 5-26 (2016) - [j33]Marwan Hassani, Thomas Seidl:
Clustering Big Data streams: recent challenges and contributions. it Inf. Technol. 58(4): 206-213 (2016) - [c222]Marwan Hassani, Pascal Spaus, Alfredo Cuzzocrea, Thomas Seidl:
I-HASTREAM: Density-Based Hierarchical Clustering of Big Data Streams and Its Application to Big Graph Analytics Tools. CCGrid 2016: 656-665 - [c221]Merih Seran Uysal, Daniel Sabinasz, Thomas Seidl:
Approximation-Based Efficient Query Processing with the Earth Mover's Distance. DASFAA (2) 2016: 165-180 - [c220]Merih Seran Uysal, Christian Beecks, Daniel Sabinasz, Jochen Schmücking, Thomas Seidl:
Efficient Query Processing using the Earth's Mover Distance in Video Databases. EDBT 2016: 389-400 - [c219]Marwan Hassani, Yifeng Lu, Thomas Seidl:
Towards an Efficient Ranking of Interval-Based Patterns. EDBT 2016: 688-689 - [c218]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Distance-based Multimedia Indexing. EDBT 2016: 722-723 - [c217]Tom De Nies, Christian Beecks, Fréderic Godin, Wesley De Neve, Grzegorz Stepien, Dörthe Arndt, Laurens De Vocht, Ruben Verborgh, Thomas Seidl, Erik Mannens, Rik Van de Walle:
Normalized Semantic Web Distance. ESWC 2016: 69-84 - [c216]Marwan Hassani, Yifeng Lu, Jens Wischnewsky, Thomas Seidl:
A geometric approach for mining sequential patterns in interval-based data streams. FUZZ-IEEE 2016: 2128-2135 - [c215]Laurens De Vocht, Christian Beecks, Ruben Verborgh, Erik Mannens, Thomas Seidl, Rik Van de Walle:
Effect of Heuristics on Serendipity in Path-Based Storytelling with Linked Data. HCI (4) 2016: 238-251 - [c214]Erik Scharwächter, Emmanuel Müller, Jonathan F. Donges, Marwan Hassani, Thomas Seidl:
Detecting Change Processes in Dynamic Networks by Frequent Graph Evolution Rule Mining. ICDM 2016: 1191-1196 - [c213]Klaus Schoeffmann, Christian Beecks, Mathias Lux, Merih Seran Uysal, Thomas Seidl:
Content-based retrieval in videos from laparoscopic surgery. Medical Imaging: Image-Guided Procedures 2016: 97861V - [c212]Roland Assam, Subramanyam Sathyanarayana, Thomas Seidl:
Infusing Geo-Recency Mixture Models for Effective Location Prediction in LBSN. SDM 2016: 855-863 - [c211]Tom De Nies, Christian Beecks, Fréderic Godin, Wesley De Neve, Grzegorz Stepien, Dörthe Arndt, Laurens De Vocht, Ruben Verborgh, Thomas Seidl, Erik Mannens, Rik Van de Walle:
A Distance-Based Approach for Semantic Dissimilarity in Knowledge Graphs. ICSC 2016: 254-257 - 2015
- [j32]Marwan Hassani, Yunsu Kim, Seungjin Choi, Thomas Seidl:
Subspace clustering of data streams: new algorithms and effective evaluation measures. J. Intell. Inf. Syst. 45(3): 319-335 (2015) - [j31]Emmanuel Müller, Ira Assent, Stephan Günnemann, Thomas Seidl, Jennifer G. Dy:
MultiClust special issue on discovering, summarizing and using multiple clusterings. Mach. Learn. 98(1-2): 1-5 (2015) - [c210]Ayman Tarakji, Marwan Hassani, Lyubomir Georgiev, Thomas Seidl, Rainer Leupers:
Parallel Density-Based Stream Clustering Using a Multi-user GPU Scheduler. BDAS 2015: 343-360 - [c209]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Gradient-based signatures for big multimedia data. IEEE BigData 2015: 2834-2835 - [c208]Daniel Töws, Marwan Hassani, Christian Beecks, Thomas Seidl:
Optimizing Sequential Pattern Mining Within Multiple Streams. BTW Workshops 2015: 223-232 - [c207]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Distance-based Multimedia Indexing. BTW Workshops 2015: 265-268 - [c206]Volker Markl, Erhard Rahm, Wolfgang Lehner, Michael Beigl, Thomas Seidl:
Big Data-Zentren - Vorstellung und Panel. BTW 2015: 477-479 - [c205]Marwan Hassani, Christian Beecks, Daniel Töws, Tatiana Serbina, Max Haberstroh, Paula Niemietz, Sabina Jeschke, Stella Neumann, Thomas Seidl:
Sequential Pattern Mining of Multimodal Streams in the Humanities. BTW 2015: 683-686 - [c204]Merih Seran Uysal, Christian Beecks, Thomas Seidl:
On efficient content-based near-duplicate video detection. CBMI 2015: 1-6 - [c203]Merih Seran Uysal, Christian Beecks, Daniel Sabinasz, Thomas Seidl:
Large-scale Efficient and Effective Video Similarity Search. LSDS-IR@CIKM 2015: 3-8 - [c202]Christian Beecks, Merih Seran Uysal, Judith Hermanns, Thomas Seidl:
Gradient-based Signatures for Efficient Similarity Search in Large-scale Multimedia Databases. CIKM 2015: 1241-1250 - [c201]Laurens De Vocht, Christian Beecks, Ruben Verborgh, Thomas Seidl, Erik Mannens, Rik Van de Walle:
Improving Semantic Relatedness in Paths for Storytelling with Linked Data on the Web. ESWC (Satellite Events) 2015: 31-35 - [c200]Christian Beecks, Marwan Hassani, Florian Obeloer, Thomas Seidl:
Efficient Distance-Based Gestural Pattern Mining in Spatiotemporal 3D Motion Capture Databases. ICDM Workshops 2015: 1425-1432 - [c199]Christian Beecks, Klaus Schoeffmann, Mathias Lux, Merih Seran Uysal, Thomas Seidl:
Endoscopic Video Retrieval: A Signature-Based Approach for Linking Endoscopic Images with Video Segments. ISM 2015: 33-38 - [c198]Christian Beecks, Marwan Hassani, Florian Obeloer, Thomas Seidl:
Efficient Query Processing in 3D Motion Capture Databases via Lower Bound Approximation of the Gesture Matching Distance. ISM 2015: 148-153 - [c197]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Earth Mover's Distance vs. Quadratic form Distance: An Analytical and Empirical Comparison. ISM 2015: 233-236 - [c196]Merih Seran Uysal, Christian Beecks, Daniel Sabinasz, Thomas Seidl:
Effective Content-Based Near-Duplicate Video Detection. ISM 2015: 254-257 - [c195]Marwan Hassani, Pascal Spaus, Alfredo Cuzzocrea, Thomas Seidl:
Adaptive Stream Clustering Using Incremental Graph Maintenance. BigMine 2015: 49-64