Automated Processing of scanned historic watermarks

A Comparison of Feature Extraction Techniques for Binarized Content-Based Image Retrieval

Bachelor thesis (2024)

Authors

S. Kho Electrical Engineering, Mathematics and Computer Science

Contributors

M. Skrodzki Computer Graphics and Visualisation - EEMCS (mentor)
Jorge Martinez (mentor)
C. Lofi Web Information Systems - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science
More Info
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Published Date

21-06-2024

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Feature extraction techniques for content-based image retrieval are explored, focusing on black-and-white images in the context of historical watermarks. Orthogonal moments and texture features are found to be most applicable. Seven methods are evaluated: four different orthogonal moments, Gabor features, and two novel combinations of orthogonal moments with Gabor features. Retrieval effectiveness is judged based on the precision-recall curve and mean average precision, and watermarks are considered when unchanged, rotated, sheared and both rotated and sheared. The results demonstrate that research into improving efficient grayscale image representation does not translate over to improvements with black-and-white images. As it stands, the basic Zernike moments and novel Gabor-Zernike features are most effective.

Files

RP_paper.pdf
(.pdf | 2.02 Mb)