DIC: Deep Image Clustering for Unsupervised Image Segmentation

Abstract

Unsupervised segmentation is an essential pre-processing technique in many computer vision tasks. However, current unsupervised segmentation techniques are sensitive to the parameters such as the segmentation numbers or of high training and inference complexity. Encouraged by neural networks' flexibility and their ability for modelling intricate patterns, an unsupervised segmentation framework based on a novel deep image clustering (DIC) model is proposed. The DIC consists of a feature transformation subnetwork (FTS) and a trainable deep clustering subnetwork (DCS) for unsupervised image clustering. FTS is built on a simple and capable network architecture. DCS can assign pixels with different cluster numbers by updating cluster associations and cluster centers iteratively. Moreover, a superpixel guided iterative refinement loss is designed to optimize the DIC parameters in an overfitting manner. Extensive experiments have been conducted on the Berkley Segmentation Database. The experimental results show that DCS is more effective in aggregating features during the clustering procedure. DIC has also proven to be less sensitive to varying segmentation parameters and of lower computation costs, and DIC can achieve significantly better segmentation performance compared to the state-of-the-art techniques. The source code is available on https://github.com/zmbhou/DIC.