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Using Variational Multi-view Learning for Classification of Grocery Items.

Marcus Klasson1, Cheng Zhang2, Hedvig Kjellström1

  • 1Division of Robotics, Perception, and Learning, Lindstedtsvägen 24, 114 28 Stockholm, Sweden.

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Summary
This summary is machine-generated.

This study introduces a new grocery dataset for computer vision assistive technologies. Combining natural images with iconic visuals and text descriptions significantly improves grocery item recognition accuracy for visually impaired individuals.

Keywords:
DSML 2: Proof-of-Concept: Data science output has been formulated, implemented, and tested for one domain/problem

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Area of Science:

  • Computer Vision
  • Assistive Technologies
  • Machine Learning

Background:

  • Visually impaired individuals face challenges recognizing objects in constrained environments like grocery stores.
  • Existing computer vision models often struggle with the nuances of grocery item recognition.

Purpose of the Study:

  • To develop and evaluate a novel dataset and methodology for improving grocery item recognition using computer vision.
  • To enhance assistive technologies for visually impaired individuals in shopping scenarios.

Main Methods:

  • Created a novel dataset of natural grocery images taken in-store.
  • Incorporated iconic images and text descriptions for each grocery item.
  • Utilized a multi-view generative model to learn lower-dimensional representations from combined data.

Main Results:

  • The proposed method, using natural images, iconic images, and text descriptions, achieved higher classification accuracies compared to using natural images alone.
  • Iconic images aided in distinguishing items based on visual differences.
  • Text descriptions proved effective in differentiating visually similar items with varying ingredients.

Conclusions:

  • Integrating multi-modal data (natural images, iconic images, text) significantly enhances grocery item recognition performance.
  • This approach offers a promising direction for developing more effective computer vision-based assistive technologies for the visually impaired.
  • The novel dataset and methodology can advance research in object recognition within specialized environments.