Pseudo Pixel-level Labeling for Images with Evolving Content

• URL: http://arxiv.org/abs/2105.09975v1
• Date: Thu, 20 May 2021 18:14:19 GMT
• Title: Pseudo Pixel-level Labeling for Images with Evolving Content
• Authors: Sara Mousavi, Zhenning Yang, Kelley Cross, Dawnie Steadman, Audris Mockus
• Abstract summary: We propose a pseudo-pixel-level label generation technique to reduce the amount of effort for manual annotation of images. 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
• Score: 5.573543601558405
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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