CLUE: Cluster-based Retrieval of Images by Unsupervised Learning

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CLUE: Cluster-based Retrieval of Images by Unsupervised Learning (2003)

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by Yixin Chen , James Z. Wang , Robert Krovetz

Venue:	IEEE Transactions on Image Processing
Citations:	63 - 3 self

BibTeX

@ARTICLE{Chen03clue:cluster-based,
    author = {Yixin Chen and James Z. Wang and Robert Krovetz},
    title = {CLUE: Cluster-based Retrieval of Images by Unsupervised Learning},
    journal = {IEEE Transactions on Image Processing},
    year = {2003},
    volume = {14},
    pages = {2005}
}

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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 di#erent 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 images of the same semantics are similar in a way, images of di#erent semantics are di#erent 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 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.

Keyphrases

cluster-based retrieval di erent semantic concept real world image data unsupervised learning high-level concept keyword-based image retrieval system image similarity measure typical cbir system image search real-valued symmetric similarity measure many current cbir system user semantic relevant clue query result general approach high feature similarity low-level feature di erent semantics ordered image novel image retrieval scheme semantic gap preliminary result feature similarity empirical result retrieving image cluster experimental image retrieval system semantic gap problem improved performance typical content-based image retrieval

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