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In this paper, we propose a dynamic approach to feature and classifier selection. In our approach, based on performance, visual features and classifiers are selected automatically. In earlier work, we presented the Visual Apprentice, in which users can define visual object models via a multiple-level object definition hierarchy (region, perceptual-area, object-part, and object). Visual Object Detectors are learned, using various learning algorithms- as the user provides examples from images or video, visual features are extracted and multiple classifiers are learned for each node of the hierarchy. In this paper, features and classifiers are selected automatically at each node, depending on their performance over the training set provided by the user. Thus, changes in the training data yield dynamic changes in the features and classifiers used. We introduce the concept of Recurrent Visual Semantics and show how it can be used to identify domains in which performancebased learning techni...

1 This report summarizes progress in image understanding research at the University of Massachusetts over the past year. Many of the individual efforts discussed in this paper are further developed in other papers in this proceedings. The summary is organized into several areas: 1. Mobile Robot Navigation 2. Motion and Stereo Processing 3. Knowledge-Based Interpretation of Static Scenes 4. Image Understanding Architecture The research program in computer vision at UMass has as one of its goals the integration of a diverse set of research efforts into a system that is ultimately intended to achieve real-time image interpretation in a variety of vision applications. 1. Mobile Robot Navigation The initial focus of the mobile robot navigation project (Fennema and Hanson 1990b) has been on the development of a system for goal oriented navigation through a partially modeled, unchanging environment which contains no unmodeled obstacles. This simplified environment is intended to provide a fou...