DocumentCode :
999978
Title :
CLUE: cluster-based retrieval of images by unsupervised learning
Author :
Chen, Yixin ; Wang, James Z. ; Krovetz, Robert
Author_Institution :
Dept. of Comput. Sci., Univ. of New Orleans, LA, USA
Volume :
14
Issue :
8
fYear :
2005
Firstpage :
1187
Lastpage :
1201
Abstract :
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.
Keywords :
content-based retrieval; image retrieval; relevance feedback; unsupervised learning; visual databases; CLUE; cluster-based retrieval of images by unsupervised learning; content-based image retrieval system; database; graph-theoretic clustering algorithm; keyword-based image retrieval systems; real-valued symmetric similarity measure; relevance feedback systems; Computer science; Content based retrieval; Histograms; Image color analysis; Image databases; Image retrieval; Information retrieval; National electric code; Spatial databases; Unsupervised learning; Content-based image retrieval (CBIR); image classification; similarity measure; spectral graph clustering; unsupervised learning; Algorithms; Artificial Intelligence; Databases, Factual; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Pattern Recognition, Automated;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2005.849770
Filename :
1468202
Link To Document :
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