Abstract
We are focusing on image classification in industrial processing taking into account the most problematic issue of the processing: the lack of labeled data. Here, we are considering three datasets: the first one is an unsorted collection of all types of manufactured products and includes 100 images per class. The second one consists of products sorted into particular classes by a specialized employee and includes only ten images per class. The last one includes a massive volume of labeled images, but it is used only for the proposal validation. As the configuration is challenging for neural networks, we propose to use Image Represented by a Fuzzy Function in order to enrich original image information. We solve the task using various autoencoder architectures and prove that such the proposal increases the autoencoders success rate.
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Acknowledgment
The work was supported from ERDF/ESF “Centre for the development of Artificial Intelligence Methods for the Automotive Industry of the region” (No. CZ.02.1.01/0.0/0.0/17_049/0008414).
For more supplementary materials and overview of our lab work see http://graphicwg.irafm.osu.cz/storage/pr/links.html.
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Hurtik, P., Molek, V. (2019). Fuzzy Preprocessing for Semi-supervised Image Classification in Modern Industry. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_1
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DOI: https://doi.org/10.1007/978-3-030-20518-8_1
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