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Novel approach to image similarity estimation and object matching: leveraging ViT architecture and euclidean distance metric

Ardehkhani, P ; Sharif University of Technology | 2024

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  1. Type of Document: Article
  2. DOI: 10.1109/AISP61396.2024.10475256
  3. Publisher: 2024
  4. Abstract:
  5. In addressing the challenge of image similarity estimation on the MNIST dataset, our research drives from conventional Siamese network methodologies by incorporating Vision Transformer (ViT) architecture. Departing from the standard MNIST dataset, we introduced a novel paired dataset tailored to enhance the capabilities of similarity estimation. The innovation lies in the utilization of ViT as the core foundation for extracting features, followed by the application of Euclidean distance metrics on the dual input. This departure from the traditional approach not only broadens the scope of image similarity assessment but also enhances the model's discriminative power. Notably, the model attains a commendable test accuracy of 97.14% with a patch size of 7, underscoring the efficacy of our proposed methodology. This work it not only adds to the changing scenery of the image similarity estimation while also emphasizing the importance of leveraging non-conventional architectures to achieve enhanced performance in this domain. © 2024 IEEE
  6. Keywords:
  7. Artificial Intelligence ; Vision Transformer ; Computer vision ; Image analysis ; Image enhancement ; Network architecture ; Deep learning ; Euclidean distance ; Euclidean distance metrics ; Extracting features ; Image similarity ; Network methodologies ; Object matching ; Similarity estimation ; Traditional approaches
  8. Source: 2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024 ; 2024 ; 979-835038394-2 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/10475256