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Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

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Related Experiment Video

Updated: Jun 18, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Accurate image search using the contextual dissimilarity measure.

Hervé Jegou1, Cordelia Schmid, Hedi Harzallah

  • 1INRIA Grenoble, France.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 21, 2009
PubMed
Summary

This study introduces a new contextual dissimilarity measure to enhance bag-of-features image search accuracy. The method improves upon standard distances and state-of-the-art approaches, showing context-dependent parameter optimization is key.

Related Experiment Videos

Last Updated: Jun 18, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

  • Computer Science
  • Image Processing
  • Machine Learning

Background:

  • Bag-of-features models are widely used for image search.
  • Standard distance measures can be suboptimal for complex image datasets.
  • Improving retrieval accuracy in large-scale image databases remains a challenge.

Purpose of the Study:

  • To introduce a novel contextual dissimilarity measure for bag-of-features image search.
  • To improve the accuracy and robustness of image retrieval systems.
  • To analyze the impact of various parameters on retrieval performance.

Main Methods:

  • Developed a contextual dissimilarity measure incorporating local vector distribution.
  • Utilized an iterative estimation approach inspired by Sinkhorn's scaling algorithm.
  • Evaluated performance on Nistér-Stewénius and Lola datasets, comparing against standard and state-of-the-art methods.

Main Results:

  • The contextual dissimilarity measure significantly improves accuracy over standard distances.
  • Outperformed existing state-of-the-art methods on benchmark datasets.
  • Optimal parameter settings, including descriptor count and vocabulary size, are context-dependent.
  • Novel variants like multiple assignment and rank aggregation further boost accuracy.

Conclusions:

  • The proposed contextual dissimilarity measure offers a substantial advancement in bag-of-features image search.
  • Parameter optimization is crucial and dataset-specific for achieving peak performance.
  • Future work can explore trade-offs between accuracy, memory, and efficiency with advanced variants.