Abstract
The visual appearance of an image is closely associated with its low-level features. Identifying the set of features that best characterizes the image is useful for tasks such as content-based image indexing and retrieval. In this paper, we present a method which simultaneously models and clusters large sets of images and their low-level visual features. A computational energy function suited for co-clustering images and their features is first constructed and a Hopfield model based stochastic algorithm is then developed for its optimization. We apply the method to cluster digital color photographs and present results to demonstrate its usefulness and effectiveness.
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
