On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Swaroop Bennabhaktula*, Joey Antonisse, George Azzopardi

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

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Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise.
Originele taal-2English
TitelComputer Analysis of Images and Patterns
SubtitelCAIP 2021
RedacteurenN Tsapatsoulis, A Panayides, T Theocharides, A Lanitis, C Pattichis, M Vento
Plaats van productieCham
Aantal pagina's11
VolumePart 1
ISBN van elektronische versie978-3-030-89128-2
ISBN van geprinte versie978-3-030-89127-5
StatusPublished - 31-okt-2021
Evenement19th International Conference, CAIP 2021 - Virtual event
Duur: 28-sep-202130-sep-2021

Publicatie series

NaamLecture Notes in Computer Science
ISSN van geprinte versie0302-9743


Conference19th International Conference, CAIP 2021

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