  Along with the breakthrough of convolutional neural networks, learning-based
segmentation has emerged in many research works. Most of them are based on
supervised learning, requiring plenty of annotated data; however, to support
segmentation, a label for each pixel is required, which is obviously expensive.
As a result, the issue of lacking annotated segmentation data commonly exists.
Continuous learning is a promising way to deal with this issue; however, it
still has high demands on human labor for annotation. What's more, privacy is
highly required in segmentation data for real-world applications, which further
calls for on-device learning. In this paper, we aim to resolve the above issue
in an alternative way: Instead of supervised segmentation, we propose to
develop efficient unsupervised segmentation that can be executed on edge
devices. Based on our observation that segmentation can obtain high performance
when pixels are mapped to a high-dimension space, we for the first time bring
brain-inspired hyperdimensional computing (HDC) to the segmentation task. We
build the HDC-based unsupervised segmentation framework, namely "SegHDC". In
SegHDC, we devise a novel encoding approach that follows the Manhattan
distance. A clustering algorithm is further developed on top of the encoded
high-dimension vectors to obtain segmentation results. Experimental results
show SegHDC can significantly surpass neural network-based unsupervised
segmentation. On a standard segmentation dataset, DSB2018, SegHDC can achieve a
28.0% improvement in Intersection over Union (IoU) score; meanwhile, it
achieves over 300x speedup on Raspberry PI. What's more, for a larger size
image in the BBBC005 dataset, the existing approach cannot be accommodated to
Raspberry PI due to out of memory; on the other hand, SegHDC can obtain
segmentation results within 3 minutes while achieving a 0.9587 IoU score.
