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Consistent and Specific Hashing for image set classification.

Xingfeng Li1, Yuan Sun2, Xuedong Li3

  • 1School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, 621010, China; Department of Computer Science, Nanjing University of Science and Technology Nanjing, Nanjing, 210094, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 18, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Consistent and Specific Hashing (CSH) for image set classification (ISC). CSH improves efficiency and accuracy by learning compact hash codes, outperforming existing methods in classification and speed.

Keywords:
Hashing aggregation strategyImage set classificationSample-specific hash codesSet-consistent hash codes

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Image set classification (ISC) leverages multiple images to enhance classification accuracy over single-image methods.
  • Existing ISC methods often face computational challenges with large datasets.
  • Hashing-based approaches offer lower computational cost but may overlook set-specific properties.

Purpose of the Study:

  • To develop an efficient and effective hashing-based method for image set classification.
  • To address limitations in existing hashing methods for ISC, particularly regarding consistency and specificity.
  • To improve the excavation of semantic information within and between image sets.

Main Methods:

  • Proposed Consistent and Specific Hashing (CSH) for ISC.
  • Utilized Hadamard matrices for pre-computed set-consistent hash codes to maximize inter-set Hamming distance.
  • Learned sample-specific hash codes and employed a hashing aggregation strategy for intra-set compactness and inter-set separability.

Main Results:

  • CSH demonstrated superior performance compared to existing methods in image set classification.
  • The proposed method achieved significant improvements in classification accuracy.
  • CSH also showed a reduction in running time, indicating computational efficiency.

Conclusions:

  • CSH effectively addresses the limitations of previous hashing-based ISC methods.
  • The method successfully excavates semantic information by preserving intra-set compactness and inter-set separability.
  • CSH offers a promising, computationally efficient solution for large-scale image set classification.