On-Device Unsupervised Image Segmentation - Yang, Junhuan; Sheng, Yi; Zhang, Yuzhou; Jiang, Weiwen; Yang, Lei (2023 60th ACM/IEEE Design Automation Conference (DAC), 2023)
Along with the breakthrough of convolutional neural networks, in particular encoder-decoder and U-Net, 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 which can be executed on edge devices without annotated data. Based on our observation that segmentation can obtain high performance when pixels are mapped to a high-dimension space using their position and color information, 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, which 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 that 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 300× 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.