CNN-Based Joint Clustering and Representation Learning with Feature Drift Compensation for Large-Scale Image Data

Understand

Given a large unlabeled set of images, how to efficiently and effectively group them into clusters based on extracted visual representations remains a challenging problem.

• To address this problem, we propose a convolutional neural network (CNN) to jointly solve clustering and representation learning in an iterative manner.
• In the proposed method, given an input image set, we first randomly pick k samples and extract their features as initial cluster centroids using the proposed CNN with an initial model pre-trained from the ImageNet dataset.
• Mini-batch k-means is then performed to assign cluster labels to individual input samples for a mini-batch of images randomly sampled from the input image set until all images are processed.