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An Online Hashing Algorithm for Image Retrieval Based on Optical-Sensor Network.

Xiao Chen1, Yanlong Li1,2, Chen Chen1

  • 1Department of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China.

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|March 11, 2023
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Summary
This summary is machine-generated.

This study introduces a novel online hashing model that preserves both local and global data features for improved image retrieval. The new method enhances retrieval efficiency in big data scenarios compared to existing algorithms.

Keywords:
balanced similaritydiscrete binary optimizationimage retrievalmanifold learningoptical-sensor network

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Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Online hashing is crucial for big data storage and retrieval, especially in optical-sensor networks.
  • Existing methods excessively use data tags, neglecting structural features, leading to lost image-streaming characteristics and reduced retrieval accuracy.
  • The need for efficient online hashing that preserves both local and global data semantics is critical.

Purpose of the Study:

  • To propose a novel online hashing model that fuses global and local dual semantics for enhanced image retrieval.
  • To address the limitations of existing algorithms that ignore the mining of data's structural features.
  • To improve the efficiency and accuracy of image retrieval in big data environments.

Main Methods:

  • Developed an anchor hash model based on manifold learning to preserve local streaming data features.
  • Constructed a global similarity matrix to constrain hash codes using balanced similarity between new and previous data.
  • Integrated global and local dual semantics within a unified framework and proposed a discrete binary-optimization solution.

Main Results:

  • The proposed algorithm effectively preserves both local and global data features in hash codes.
  • Experiments on CIFAR10, MNIST, and Places205 datasets demonstrate significant improvements in image retrieval efficiency.
  • The new model outperforms several existing advanced online-hashing algorithms.

Conclusions:

  • The proposed online hashing model successfully fuses global and local dual semantics, enhancing image retrieval.
  • This approach effectively addresses the shortcomings of tag-reliant hashing methods.
  • The algorithm offers a promising solution for efficient and accurate image retrieval in big data applications.