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Deep Neural Networks for Image-Based Dietary Assessment
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Ki-Cook: clustering multimodal cooking representations through knowledge-infused learning.

Revathy Venkataramanan1, Swati Padhee2, Saini Rohan Rao3

  • 1Department of Computer Science, Artificial Intelligence Research Institute, University of South Carolina, Columbia, SC, United States.

Frontiers in Big Data
|August 9, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces Ki-Cook, a new network for clustering recipes using detailed information like ingredients and titles, improving food image retrieval and understanding recipe similarity, especially for rare ingredients.

Keywords:
clusteringcooking process modelingcross-modal retrievalingredient predictionknowledge-infused learningmultimodal learningrepresentation learning

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Cross-modal recipe retrieval enables image-to-text and text-to-image searches.
  • Current methods cluster recipes based on class names, which is insufficient due to variations within and similarities between classes.
  • Detailed recipe information, including title and ingredients, offers a more robust basis for similarity determination.

Purpose of the Study:

  • To develop a novel method for clustering recipe representations that captures deeper recipe similarities.
  • To improve the accuracy of retrieving relevant information for unknown food images.
  • To enhance the understanding of recipe relationships by focusing on ingredients, particularly rare ones.

Main Methods:

  • Proposed a knowledge-infused multimodal cooking representation learning network, Ki-Cook.
  • Incorporated recipe titles, ingredients (with emphasis on rare ingredients), and cooking actions into the representation learning.
  • Utilized ingredient images within the network to learn multimodal cooking representations.
  • Built the network on the procedural attributes of the cooking process.

Main Results:

  • The Ki-Cook model demonstrated a 12% improvement in Coverage of Ground Truth and a 10% improvement in Intersection Over Union for ingredient retrieval tasks compared to baseline models.
  • Learned representations contained an average of 15.33% more rare ingredients than baseline models.
  • Achieved a 39% improvement in clustering similar recipes in the latent space, with a Fleiss kappa score of 0.35 for inter-annotator agreement.

Conclusions:

  • Leveraging comprehensive recipe details, including rare ingredients, significantly enhances recipe similarity clustering and retrieval.
  • The Ki-Cook network provides a more effective approach to multimodal recipe representation learning.
  • This work represents a significant advancement in understanding and retrieving recipe information from visual and textual data.