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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Dataset for image classification with knowledge.

Franck Anaël Mbiaya1,2, Christel Vrain1, Frédéric Ros2

  • 1University Orleans, INSA Centre Val de Loire, LIFO, EA 4022, France.

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Summary
This summary is machine-generated.

This study introduces new image classification datasets incorporating prior knowledge, improving performance with limited data. Frequent itemset mining extracts rules from attributes for enhanced deep learning models.

Keywords:
Computer visionDeep learningImage classificationKnowledgeRules

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning excels in image classification with large datasets.
  • Performance declines significantly with limited data.
  • Fine-grained classification is challenging for deep architectures.

Purpose of the Study:

  • To address the limitations of deep learning in low-data and fine-grained image classification scenarios.
  • To introduce novel datasets that integrate a priori knowledge.
  • To facilitate research on leveraging prior knowledge in image classification.

Main Methods:

  • Datasets were constructed from existing multilabel, multiclass classification, or object detection data.
  • Frequent closed itemset mining was employed to generate classes and attributes.
  • A priori knowledge was extracted in the form of rules based on these attributes.

Main Results:

  • The developed datasets integrate a priori knowledge, enhancing image classification capabilities.
  • The methodology enables the creation of structured knowledge from raw data.
  • The rule generation algorithm is detailed for practical application.

Conclusions:

  • Integrating a priori knowledge into datasets is crucial for improving deep learning performance in data-scarce and complex classification tasks.
  • The proposed method offers a viable approach to generating such datasets.
  • This work expands the available resources for research in knowledge-enhanced image classification.