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SIGMOD
2003
ACM

Qcluster: Relevance Feedback Using Adaptive Clustering for Content-Based Image Retrieval

9 years 8 months ago
Qcluster: Relevance Feedback Using Adaptive Clustering for Content-Based Image Retrieval
The learning-enhanced relevance feedback has been one of the most active research areas in content-based image retrieval in recent years. However, few methods using the relevance feedback are currently available to process relatively complex queries on large image databases. In the case of complex image queries, the feature space and the distance function of the user's perception are usually different from those of the system. This difference leads to the representation of a query with multiple clusters (i.e., regions) in the feature space. Therefore, it is necessary to handle disjunctive queries in the feature space. In this paper, we propose a new content-based image retrieval method using adaptive classification and clustermerging to find multiple clusters of a complex image query. When the measures of a retrieval method are invariant under linear transformations, the method can achieve the same retrieval quality regardless of the shapes of clusters of a query. Our method achi...
Deok-Hwan Kim, Chin-Wan Chung
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2003
Where SIGMOD
Authors Deok-Hwan Kim, Chin-Wan Chung
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