CLUE: Cluster-based Retrieval of Images by Unsupervised Learning

Yixin Chen
University of New Orleans

James Z. Wang
The Pennsylvania State University

Robert Krovetz

Abstract:

In a typical content-based image retrieval (CBIR) system, query results are a set of images sorted by feature similarities with respect to the query. However, images with high feature similarities to the query may be very different from the query in terms of semantics. This discrepancy between low-level features and high-level concepts is known as the semantic gap. This paper introduces a novel image retrieval scheme, CLUster-based rEtrieval of images by unsupervised learning (CLUE), which attempts to tackle the semantic gap problem based on a hypothesis that {\it images of the same semantics are similar in a way, images of different semantics are different in their own ways}. CLUE attempts to capture semantic concepts by learning the way that images of the same semantics are similar and retrieving image clusters instead of a set of ordered images. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. Therefore, the clusters give the algorithm as well as the users semantic relevant {\it clues} as to where to navigate. CLUE is a general approach that can be combined with any real-valued symmetric similarity measure (metric or nonmetric). Thus it may be embedded in many current CBIR systems. An experimental image retrieval system using CLUE has been implemented. The performance of the system is evaluated on a database of about $60,000$ images from COREL. Empirical results demonstrate improved performance compared with a typical CBIR system using the same image similarity measure. In addition, preliminary 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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Citation: Yixin Chen, James Z. Wang and Robert Krovetz ``CLUE: Cluster-based Retrieval of Images by Unsupervised Learning,'' IEEE Transactions on Image Processing, vol. 14, no. 8, pp. 1187-1201, 2005.

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