CLUE: Cluster-based retrieval of images by unsupervised learning

Yixin Chen, James Z. Wang, Robert Krovetz

Research output: Contribution to journalArticlepeer-review

250 Scopus citations


In a typical content-based image retrieval (CBIR) system, target images (images in the database) are sorted by feature similarities with respect to the query. Similarities among target images are usually ignored. This paper introduces a new technique, cluster-based retrieval of images by unsupervised learning (CLUE), for improving user interaction with image retrieval systems by fully exploiting the similarity information. CLUE retrieves image clusters by applying a graph-theoretic clustering algorithm to a collection of images in the vicinity of the query. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. CLUE can be combined with any real-valued symmetric similarity measure (metric or nonmetric). Thus, it may be embedded in many current CBIR systems, including relevance feedback systems. The performance of an experimental image retrieval system using CLUE is evaluated on a database of around 60,000 images from COREL. Empirical results demonstrate improved performance compared with a CBIR system using the same image similarity measure. In addition, results on images returned by Google's Image Search reveal the potential of applying CLUE to real-world image data and integrating CLUE as a part of the interface for keyword-based image retrieval systems.

Original languageEnglish (US)
Pages (from-to)1187-1201
Number of pages15
JournalIEEE Transactions on Image Processing
Issue number8
StatePublished - Aug 2005

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of 'CLUE: Cluster-based retrieval of images by unsupervised learning'. Together they form a unique fingerprint.

Cite this