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Similarity Search and Applications

17th International Conference, SISAP 2024, Providence, RI, USA, November 4–6, 2024, Proceedings

  • Conference proceedings
  • © 2025

Overview

Editors:
  1. Edgar Chávez

    1. Center for Scientific Research and Higher Education at Ensenada, Ensenada, Mexico

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  2. Benjamin Kimia

    1. Brown University, Providence, USA

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  3. Jakub Lokoč

    1. Charles University, Prague, Czech Republic

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  4. Marco Patella

    1. University of Bologna, Bologna, Italy

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  5. Jan Sedmidubsky

    1. Masaryk University, Brno, Czech Republic

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 15268)

Included in the following conference series:

Conference proceedings info: SISAP 2024.

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About this book

This book constitutes the refereed proceedings of the 17th International Conference on Similarity Search and Applications, SISAP 2024, held in Providence, RI, USA, during November 4–6, 2024.

The 13 full papers, 7 short papers and 4 Indexing Challenge papers included in this book were carefully reviewed and selected from 32 submissions. They focus on efficient similarity search methods addressing the challenges of exploring similar items and managing vast machine-learning data sets efficiently. 

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Table of contents (25 papers)

  1. Front Matter

    Pages i-xv
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  2. Research Track

    1. Front Matter

      Pages 1-1
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    2. An Efficient Framework for Approximate Nearest Neighbor Search on High-Dimensional Multi-metric Data

      • Reon Uemura, Daichi Amagata, Takahiro Hara
      Pages 3-17

    3. REHAB24-6: Physical Therapy Dataset for Analyzing Pose Estimation Methods

      • Andrej Černek, Jan Sedmidubsky, Petra Budikova
      Pages 18-33

    4. ETDD70: Eye-Tracking Dataset for Classification of Dyslexia Using AI-Based Methods

      • Jan Sedmidubsky, Nicol Dostalova, Roman Svaricek, Wolf Culemann
      Pages 34-48

    5. Demonstrating the Efficacy of Polyadic Queries

      • Ben Claydon, Richard Connor, Alan Dearle, Lucia Vadicamo
      Pages 49-56

    6. Scalable Polyadic Queries

      • Richard Connor, Alan Dearle, Ben Claydon
      Pages 57-64

    7. A Dynamic Evaluation Metric for Feature Selection

      • Muhammad Rajabinasab, Anton D. Lautrup, Tobias Hyrup, Arthur Zimek
      Pages 65-72

    8. Personalized Similarity Models for Evaluating Rehabilitation Exercises from Monocular Videos

      • Miriama Jánošová, Petra Budikova, Jan Sedmidubsky
      Pages 73-87

    9. Impact of the Neighborhood Parameter on Outlier Detection Algorithms

      • Félix Iglesias, Conrado Martínez, Tanja Zseby
      Pages 88-96

    10. Optimizing CLIP Models for Image Retrieval with Maintained Joint-Embedding Alignment

      • Konstantin Schall, Kai Uwe Barthel, Nico Hezel, Klaus Jung
      Pages 97-110

    11. Bayesian Estimation Approaches for Local Intrinsic Dimensionality

      • Zaher Joukhadar, Hanxun Huang, Sarah Monazam Erfani, Ricardo J. G. B. Campello, Michael E. Houle, James Bailey
      Pages 111-125

    12. Towards Personalized Similarity Search for Vector Databases

      • Marek Mahrík, Matúš Šikyňa, Vladimir Mic, Pavel Zezula
      Pages 126-139

    13. Information Dissimilarity Measures in Decentralized Knowledge Distillation: A Comparative Analysis

      • Mbasa Joaquim Molo, Lucia Vadicamo, Emanuele Carlini, Claudio Gennaro, Richard Connor
      Pages 140-154

    14. An Empirical Evaluation of Search Strategies for Locality-Sensitive Hashing: Lookup, Voting, and Natural Classifier Search

      • Malte Helin Johnsen, Martin Aumüller
      Pages 155-169

    15. On the Design of Scalable Outlier Detection Methods Using Approximate Nearest Neighbor Graphs

      • Camilla Birch Okkels, Martin Aumüller, Arthur Zimek
      Pages 170-184

    16. A Topological Evaluation Model for Manifold Learning and Embedding Techniques

      • Victor Reyes, Margarita Liarou, Stephane Marchand-Maillet
      Pages 185-192

    17. Local Intrinsic Dimensionality and the Convergence Order of Fixed-Point Iteration

      • Michael E. Houle, Vincent Oria, Hamideh Sabaei
      Pages 193-206

    18. Identifying Propagating Signals with Spatio-Temporal Clustering in Multivariate Time Series

      • Jan David Hüwel, Georg Stefan Schlake, Kevin Albrechts, Christian Beecks
      Pages 207-214

    19. Robust Statistical Scaling of Outlier Scores: Improving the Quality of Outlier Probabilities for Outliers

      • Philipp Röchner, Henrique O. Marques, Ricardo J. G. B. Campello, Arthur Zimek, Franz Rothlauf
      Pages 215-222
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Other volumes

  1. Similarity Search and Applications

Editors and Affiliations

  • Center for Scientific Research and Higher Education at Ensenada, Ensenada, Mexico

    Edgar Chávez

  • Brown University, Providence, USA

    Benjamin Kimia

  • Charles University, Prague, Czech Republic

    Jakub Lokoč

  • University of Bologna, Bologna, Italy

    Marco Patella

  • Masaryk University, Brno, Czech Republic

    Jan Sedmidubsky

Bibliographic Information

  • Book Title: Similarity Search and Applications

  • Book Subtitle: 17th International Conference, SISAP 2024, Providence, RI, USA, November 4–6, 2024, Proceedings

  • Editors: Edgar Chávez, Benjamin Kimia, Jakub Lokoč, Marco Patella, Jan Sedmidubsky

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-75823-2

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2025

  • Softcover ISBN: 978-3-031-75822-5Published: 25 October 2024

  • eBook ISBN: 978-3-031-75823-2Published: 24 October 2024

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XV, 302

  • Number of Illustrations: 8 b/w illustrations, 78 illustrations in colour

  • Topics: Information Storage and Retrieval, Database Management, Data Mining and Knowledge Discovery, Machine Learning, Computer Applications

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