ProFeat: Unsupervised image clustering via progressive feature refinement

Jeonghoon Kim, Sunghoon Im, Sunghyun Cho

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Unsupervised image clustering is a chicken-and-egg problem that involves representation learning and clustering. To resolve the inter-dependency between them, many approaches that iteratively perform the two tasks have been proposed, but their accuracy is limited due to inaccurate intermediate representations and clusters. To overcome this, this paper proposes ProFeat, a novel iterative approach to unsupervised image clustering based on progressive feature refinement. To learn discriminative features for clustering while avoiding adversarial influence from inaccurate intermediate clusters, ProFeat rigorously divides representation learning and clustering by modeling a neural network for clustering as a composition of an embedding and a clustering function and introducing an auxiliary embedding function. ProFeat progressively refines representations using confident samples from intermediate clusters using an extended contrastive loss. This paper also proposes ensemble-based feature refinement for more robust clustering. Our experiments demonstrate that ProFeat achieves superior results compared to previous methods.

Original languageEnglish
Pages (from-to)166-172
Number of pages7
JournalPattern Recognition Letters
Volume164
DOIs
StatePublished - Dec 2022

Bibliographical note

Publisher Copyright:
© 2022

Keywords

  • Clustering
  • Representation learning
  • Unsupervised learning

Fingerprint

Dive into the research topics of 'ProFeat: Unsupervised image clustering via progressive feature refinement'. Together they form a unique fingerprint.

Cite this