Pseudo Pixel-level Labeling for Images with Evolving Content. (arXiv:2105.09975v1 [cs.CV])

Annotating images for semantic segmentation requires intense manual labor and
is a time-consuming and expensive task especially for domains with a scarcity
of experts, such as Forensic Anthropology. We leverage the evolving nature of
images depicting the decay process in human decomposition data to design a
simple yet effective pseudo-pixel-level label generation technique to reduce
the amount of effort for manual annotation of such images. We first identify
sequences of images with a minimum variation that are most suitable to share
the same or similar annotation using an unsupervised approach. Given one
user-annotated image in each sequence, we propagate the annotation to the
remaining images in the sequence by merging it with annotations produced by a
state-of-the-art CAM-based pseudo label generation technique. To evaluate the
quality of our pseudo-pixel-level labels, we train two semantic segmentation
models with VGG and ResNet backbones on images labeled using our pseudo
labeling method and those of a state-of-the-art method. The results indicate
that using our pseudo-labels instead of those generated using the
state-of-the-art method in the training process improves the mean-IoU and the
frequency-weighted-IoU of the VGG and ResNet-based semantic segmentation models
by 3.36%, 2.58%, 10.39%, and 12.91% respectively.



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