CLUE: Cluster-based retrieval of images by unsupervised learning

被引:175
作者
Chen, YX [1 ]
Wang, JZ
Krovetz, R
机构
[1] Univ New Orleans, Dept Comp Sci, New Orleans, LA 70148 USA
[2] Res Inst Children, New Orleans, LA 70118 USA
[3] Penn State Univ, Sch Informat Sci & Technol, University Pk, PA 16802 USA
[4] Penn State Univ, Dept Comp Sci & Engn, University Pk, PA 16802 USA
[5] Teoma Technol, Piscataway, NJ 08554 USA
基金
美国国家科学基金会;
关键词
content-based image retrieval (CBIR); image classification; similarity measure; spectral graph clustering; unsuperised learning;
D O I
10.1109/TIP.2005.849770
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:1187 / 1201
页数:15
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