Increasing image segmentation accuracy on small datasets by merging multiple inferences on augmented images

Venkateswara R. Dasari; Billy E. Geerhart; David M. Alexander

doi:10.1117/12.2618571

30 May 2022 Increasing image segmentation accuracy on small datasets by merging multiple inferences on augmented images

Venkateswara R. Dasari, Billy E. Geerhart, David M. Alexander

Proceedings Volume 12117, Disruptive Technologies in Information Sciences VI; 1211709 (2022) https://doi.org/10.1117/12.2618571
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Supervised machine learning depends on training a model to mimic previous labeled results. The problem with a small dataset is that data augmentation is necessary to increase the generalization of the model to future images, but we have observed that future images won’t necessarily be in the same domain as the augmented images. To alleviate this problem we applied image segmentation multiple times on the same image by using the same data augmentation techniques on the image in question, and then we merged the results using a priority based on the class weights used when training the model. Merging the segmentation results from the augmented images increased the mean-intersection-over-union over the inference results that used a single image.

Citation Download Citation

Venkateswara R. Dasari, Billy E. Geerhart, and David M. Alexander "Increasing image segmentation accuracy on small datasets by merging multiple inferences on augmented images", Proc. SPIE 12117, Disruptive Technologies in Information Sciences VI, 1211709 (30 May 2022); https://doi.org/10.1117/12.2618571

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KEYWORDS

Image segmentation

Data modeling

Algorithm development

Image processing algorithms and systems

Reverse modeling

Statistical modeling

Image processing

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