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Categorization of Images Using Autoencoder Hashing and Training of Intra Bin Classifiers for Image Classification and

P Mercy Rajaselvi Beaulah1, D Manjula2, Vijayan Sugumaran3,4

  • 1Department of Computer science & Engineering, Easwari Engineering College, Chennai, India. mercyeec@gmail.com.

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|June 13, 2018
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Summary
This summary is machine-generated.

This study introduces autoencoder hashing for effective image annotation in large-scale image retrieval. The novel approach improves accuracy and performance over traditional methods.

Keywords:
Autoencoder hashingDAG SVMImage annotationIntra bin classifiersMicro-structure descriptorTag weight

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Automatic image annotation is crucial for image retrieval.
  • Traditional methods struggle with large, related image classes due to complexity and accuracy issues.

Purpose of the Study:

  • To propose a novel autoencoder hashing approach for categorizing images in large-scale datasets.
  • To enhance the accuracy and efficiency of automatic image annotation.

Main Methods:

  • Developed an autoencoder hashing technique for image categorization.
  • Implemented intra-bin classifiers for query image classification.
  • Computed tag weight and tag frequency for improved annotation.

Main Results:

  • The proposed autoencoder hashing method demonstrated superior performance.
  • Achieved higher precision, accuracy, mean average precision (MAP), and F1 score compared to existing methods.

Conclusions:

  • Autoencoder hashing offers a more effective solution for large-scale image annotation.
  • The novel approach addresses limitations of traditional computationally complex methods.