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Related Experiment Video

Updated: Jul 13, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Modeling semantic aspects for cross-media image indexing.

Florent Monay1, Daniel Gatica-Perez

  • 1Signal Processing Institute, Ecole Polytechnique Fédérale de Lausanne, Switzerland. florent.monay@epfl.ch

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 19, 2007
PubMed
Summary

This study introduces new Probabilistic Latent Semantic Analysis (PLSA) models for semantic image indexing. Text-based analysis proved superior for creating meaningful latent spaces and improving image annotation.

Related Experiment Videos

Last Updated: Jul 13, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Information Retrieval

Background:

  • Current image retrieval often relies on query-by-example, limiting intuitive search.
  • Semantic indexing of large image collections for text-based search remains a challenge.
  • Existing models struggle to effectively learn visual-textual dependencies for automatic indexing.

Purpose of the Study:

  • To develop and evaluate novel Probabilistic Latent Semantic Analysis (PLSA) models for automatic image indexing.
  • To investigate different approaches for learning latent aspects from annotated images.
  • To propose an improved image representation for enhanced semantic understanding.

Main Methods:

  • Developed three alternative PLSA learning procedures for annotated images.
  • Proposed a new image representation combining quantized local color and texture descriptors.
  • Investigated standard EM algorithm and two asymmetric PLSA learning approaches.
  • Constrained latent space definition to either visual or textual modalities.

Main Results:

  • Textual modality-based learning yielded a more semantically meaningful latent space.
  • The proposed methods demonstrated improved performance in automatic image annotation.
  • The new image representation outperformed traditional Blob histograms.
  • Comparative evaluation showed the framework's validity against recent methods.

Conclusions:

  • Asymmetric PLSA learning, particularly text-constrained, is effective for semantic image indexing.
  • The proposed discriminative image representation enhances semantic analysis.
  • The developed framework offers a robust solution for intuitive text-based image search.