A Decision-theoretic View of Image Retrieval

Monday, January 29, 2001 - 3:30pm - 4:30pm
Lind 400
Nuno Vasconcelos (Schlumberger Cambridge Research)
The design of an effective architecture for image retrieval requires careful consideration of the interplay between feature selection, feature representation, and similarity function. We introduce a decision theoretic formulation of the retrieval problem that establishes guidelines for the joint design of all these components leading to a Bayesian architecture with minimum probability of retrieval error. This architecture is shown to generalize a significant number of previous approaches, solving some of the most challenging problems faced by these: joint modeling of color and texture, explicit control of the trade-off between feature transformation and feature representation, good invariance properties, and unified support for local and global queries without image segmentation. Extensive experimental results show that Bayesian retrieval performs well on color, texture, and generic image databases in terms of both retrieval accuracy and perceptual relevance of similarity judgments.