Abstract
In addition to photo data, many digital cameras and smartphones capture Exif metadata which contain information about the camera parameters used when a photo was captured. While most semantic image recognition approaches only use pixel data for classification decisions, this work aims to examine whether Exif data can improve image classification performed by Convolutional Neural Networks (CNNs). We compare the classification performance and training time of fusion models that use both, image data and Exif metadata, for image classification in contrast to models that use only image data. The most promising result was obtained with a fusion model which was able to increase the classification accuracy for the selected target concepts by \(7.5\%\) compared to the baseline, while the average total training time of all fusion models was reduced by \(7.9\%\).
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Notes
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The dataset containing references to the Flickr images has been published with the DOI “10.48564/unibafd-vmm4f-m4y33”. The source code associated with this publication has also been published with the DOI “10.48564/unibafd-q73v9-wz721”. The corresponding GitHub repository can be accessed through the provided references.
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Lederer, R., Bullin, M., Henrich, A. (2023). Exploiting Exif Data to Improve Image Classification Using Convolutional Neural Networks. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing – ICIAP 2023. ICIAP 2023. Lecture Notes in Computer Science, vol 14233. Springer, Cham. https://doi.org/10.1007/978-3-031-43148-7_40
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