TY - JOUR
T1 - CLUE
T2 - Cluster-based retrieval of images by unsupervised learning
AU - Chen, Yixin
AU - Wang, James Z.
AU - Krovetz, Robert
N1 - Funding Information:
Manuscript received November 21, 2003; revised July 23, 2004. This work was supported in part by the National Science Foundation under Grant IIS-0219272 and CNS-0202007, The Pennsylvania State University, the PNC Foundation, SUN Microsystems under Grant EDUD-7824-010456-US, the University of New Orleans, The Research Institute for Children, NASA/EP-SCoR DART under Grant NCC5-573, and the NEC Research Institute. This research was done when R. Krovetz was with NEC Research Institute, Princeton, NJ. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Gopal Pingali.
PY - 2005/8
Y1 - 2005/8
N2 - 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.
AB - 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.
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U2 - 10.1109/TIP.2005.849770
DO - 10.1109/TIP.2005.849770
M3 - Article
C2 - 16121465
AN - SCOPUS:24144495321
SN - 1057-7149
VL - 14
SP - 1187
EP - 1201
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 8
ER -