This paper describes PicHunter, an image retrieval system that implements a novel approach to relevance feedback, such that the entire history of user selections contributes to the system's estimate of the user's goal image. To accomplish this, PicHunter uses Bayesian learning based on a probabilistic model of a user's behavior. The predictions of this model are combined with the selections made during a search to estimate the probability associated with each image. These probabilities are then used to select images for display. Details of our model of a user's behavior were tuned using an off-line leaning algorithm. For clarity, our studies were done with the simplest possible user interface but the algorithm can easily be incorporated into systems which support complex queries, including most previously proposed systems. However, even with this constraint and simple image features, PicHunter is able to locate randomly selected targets in a database of 4522 images after displaying an average of only 55 groups of 4 images which is over 10 times better than chance. We therefore expect that the performance of current image database retrieval systems can be improved by incorporation of the techniques described here.

Sather extends the notion of an iterator in a powerful new way. We argue that iteration abstractions belong in class interfaces on an equal footing with routines. Sather iterators were derived from CLU iterators but are much more flexible and better suited for object-oriented programming. We retain the property that iterators are structured , i.e., strictly bound to a controlling structured statement. We motivate and describe the construct along with several simple examples. We compare it with iteration based on CLU iterators, cursors, riders, streams, series, generators, coroutines, blocks, closures, and lambda expressions. Finally, we describe experiences with iterators in the Sather compiler and libraries.

A technique for representing and learning smooth nonlinear manifolds is presented and applied to several lip reading tasks. Given a set of points drawn from a smooth manifold in an abstract feature space, the technique is capable of determining the structure of the surface and of finding the closest manifold point to a given query point. We use this technique to learn the "space of lips" in a visual speech recognition task. The learned manifold is used for tracking and extracting the lips, for interpolating between frames in an image sequence and for providing features for recognition. We describe a system based on hidden Markov models and this learned lip manifold that significantly improves the performance of acoustic speech recognizers in degraded environments. We also present preliminary results on a purely visual lip reader.>

The problem of interpolating between specified images in an image sequence is a simple, but important task in model-based vision. We describe an approach based on the abstract task of "manifold learning" and present results on both synthetic and real image se(cid:173) quences. This problem arose in the development of a combined lip-reading and speech recognition system.