A Hybrid Framework for Matching Printing Design Files to Product Photos

Year 2020, Volume: 8 Issue: 2, 170 - 180, 30.04.2020

Alper Kaplan , Erdem Akagunduz

https://doi.org/10.17694/bajece.677326

Abstract

We propose a real-time image matching framework, which is hybrid in the sense that it uses both hand-crafted features and deep features obtained from a well-tuned deep convolutional network. The matching problem, which we concentrate on, is specific to a certain application, that is, printing design to product photo matching. Printing designs are any kind of template image files, created using a design tool, thus are perfect image signals. However, photographs of a printed product suffer many unwanted effects, such as uncontrolled shooting angle, uncontrolled illumination, occlusions, printing deficiencies in color, camera noise, optic blur, et cetera. For this purpose, we create an image set that includes printing design and corresponding product photo pairs with collaboration of an actual printing facility. Using this image set, we benchmark various hand-crafted and deep features for matching performance and propose a framework in which deep learning is utilized with highest contribution, but without disabling real-time operation using an ordinary desktop computer.

Keywords

image matching, hand-crafted features, deep features, semantic segmentation, product image processing

Supporting Institution

TUBITAK - TEYDEB

Project Number

7170364

Thanks

The authors would like to thank the owner of the project, Şans Printing Industries (bidolubaski.com) for their support and hard-work.

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