Conventional unsupervised image segmentation methods return many superpixels or object parts and thus tend to over-segmentation. In this paper, we present a novel post-processing approach for ...

In follow-up work, we plan to design an unsupervised image segmentation model by combining clustering with deep learning. It will use the feature extracted by a CNN for clustering, the clustering ...

Object-based classification is a rapidly developing paradigm in image analysis. Unlike pixel-based techniques which only use the layer values, the object-based techniques can also use shape and ...

The data-hungry approach of supervised classification drives the interest of the researchers toward unsupervised approaches, especially for problems such as medical image segmentation, where labeled ...

This code is inspired from the project pytorch-unsupervised-segmentation by kanezaki. The original project is based on the paper "Unsupervised Image Segmentation by Backpropagation" presented at IEEE ...