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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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Co-attention enabled content-based image retrieval.

Zechao Hu1, Adrian G Bors1

  • 1Department of Computer Science, University of York, York YO10 5GH, UK.

Neural Networks : the Official Journal of the International Neural Network Society
|May 10, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a query-sensitive co-attention mechanism for content-based image retrieval (CBIR). It improves image matching accuracy, especially in challenging scenarios with varied image conditions.

Keywords:
ClusteringCo-attentionContent-based image retrieval

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Image Processing

Background:

  • Content-based image retrieval (CBIR) relies on effective feature extraction for accurate image similarity assessment.
  • Existing methods often use query-non-sensitive spatial weighting, leading to suboptimal performance with non-salient targets or distractors.

Purpose of the Study:

  • To propose an efficient query-sensitive co-attention mechanism for large-scale CBIR.
  • To enhance retrieval accuracy by addressing limitations of query-non-sensitive approaches.

Main Methods:

  • Developed a query-sensitive co-attention mechanism for CBIR.
  • Integrated feature clustering to mitigate computational overhead associated with query sensitivity.
  • Evaluated the co-attention maps for similarity comparison.

Main Results:

  • The proposed co-attention maps achieved superior retrieval results on benchmark datasets.
  • Demonstrated effectiveness in challenging situations, including differing image acquisition conditions between query and match images.
  • Outperformed traditional methods in identifying relevant images.

Conclusions:

  • Query-sensitive co-attention is a highly effective mechanism for improving CBIR performance.
  • The method offers a computationally efficient solution for large-scale image retrieval tasks.
  • Addresses key limitations in current CBIR feature extraction and spatial weighting strategies.