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Chapter 4 Image clustering enhanced with refined image classification

  • Md Farhad Mokter and JungHwan Oh
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Imaging Science
This chapter is in the book Imaging Science

Abstract

Image clustering is an essential analysis tool in machine learning and computer vision and can benefit many applications including content-based image annotation and image retrieval. Typically, image clustering methods are based on an objective function calculating a difference between images by using a feature extracted from each image. Recently, convolutional neural network (CNN) is used for this purpose. Even if we are using these CNN features, we are still facing two issues. First, it is difficult to find a correct number of clusters initially. The quality of the clustering result heavily depends on the accurate selection of initial number of clusters. Second, there are two images with semantically different contents but have a similar color distribution. So, a clustering algorithm clusters these two images into a same cluster and generates incorrect clustering result. In this chapter we propose a framework for image clustering with refined image classification by using CNN multiple times, which addresses these problems. The experimental results present that the proposed framework improves image clustering quality significantly.

Abstract

Image clustering is an essential analysis tool in machine learning and computer vision and can benefit many applications including content-based image annotation and image retrieval. Typically, image clustering methods are based on an objective function calculating a difference between images by using a feature extracted from each image. Recently, convolutional neural network (CNN) is used for this purpose. Even if we are using these CNN features, we are still facing two issues. First, it is difficult to find a correct number of clusters initially. The quality of the clustering result heavily depends on the accurate selection of initial number of clusters. Second, there are two images with semantically different contents but have a similar color distribution. So, a clustering algorithm clusters these two images into a same cluster and generates incorrect clustering result. In this chapter we propose a framework for image clustering with refined image classification by using CNN multiple times, which addresses these problems. The experimental results present that the proposed framework improves image clustering quality significantly.

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Contents VII
  4. Section: Image processing
  5. Chapter 1 Magnetic resonance image re-parameterization on real data 1
  6. Chapter 2 Denoising and gradient fusion for effective edge detection for noisy color images 17
  7. Chapter 3 Understanding driver attention to objects for ADASs: what do drivers see? 39
  8. Chapter 4 Image clustering enhanced with refined image classification 59
  9. Chapter 5 AI-powered framework for objective scoring of product design innovation 89
  10. Section: Computer vision
  11. Chapter 6 Image inpainting using GAN transformerbased model 111
  12. Chapter 7 Enhanced image watermarking through cross-attention and noise-invariant domain learning 127
  13. Chapter 8 Online melt pool monitoring using a deep transformer image processing solution 153
  14. Chapter 9 Implementation of deep learning techniques on thermal image classification 173
  15. Chapter 10 Drishti: a generative AI-based application for gesture recognition and execution 203
  16. Section: Pattern recognition
  17. Chapter 11 Exploring muzzle biometrics: a deep learning framework for noninvasive cattle recognition 239
  18. Chapter 12 Utilizing real-world data to develop a userindependent sensor-based human activity recognition system 253
  19. Index 273
https://doi.org/10.1515/9783111436425-004

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Contents VII
  4. Section: Image processing
  5. Chapter 1 Magnetic resonance image re-parameterization on real data 1
  6. Chapter 2 Denoising and gradient fusion for effective edge detection for noisy color images 17
  7. Chapter 3 Understanding driver attention to objects for ADASs: what do drivers see? 39
  8. Chapter 4 Image clustering enhanced with refined image classification 59
  9. Chapter 5 AI-powered framework for objective scoring of product design innovation 89
  10. Section: Computer vision
  11. Chapter 6 Image inpainting using GAN transformerbased model 111
  12. Chapter 7 Enhanced image watermarking through cross-attention and noise-invariant domain learning 127
  13. Chapter 8 Online melt pool monitoring using a deep transformer image processing solution 153
  14. Chapter 9 Implementation of deep learning techniques on thermal image classification 173
  15. Chapter 10 Drishti: a generative AI-based application for gesture recognition and execution 203
  16. Section: Pattern recognition
  17. Chapter 11 Exploring muzzle biometrics: a deep learning framework for noninvasive cattle recognition 239
  18. Chapter 12 Utilizing real-world data to develop a userindependent sensor-based human activity recognition system 253
  19. Index 273
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