ITR: Learning and Measuring Perceptual Similarity

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Image retrieval has been an active research area for many years, buttwo fundamental problems remain largely unsolved: 1) How best tolearn users' subjective query concepts, and 2) How to measure perceptualsimilarity with significant accuracy. The first problem concerns thecompleteness of formulating a query concept, e.g., how to formulate aquery such as ``animals,'' ``cathedrals,'' or ``aircraft.'' The secondproblem concerns search accuracy, i.e., given a learned query concept,how to find all images that match that concept.To tackle these two fundamental problems and to ensure that oursolutions are scalable, this project has four specific targets.First, we plan to develop novel active learning algorithms thatquickly learn users' subjective query concepts (thoughts and intents)despite time and sample constraints. Second, we will designsemi-automatic image annotation and annotation refinement methodsfor assigning semantic labels to images in order to support multimodalityquery-concept learning and information retrieval. Third, we willdevise perceptual distance functions for improving accuracy of visualsearches. For instance, once a query concept such as ``enemy vessels''is learned, we want to find every matching object in the surveillancedatabase, not missing any. Finally, we plan to conduct validationstudies} on developed learning algorithms, using experimental dataprovided by colleagues at various institutions (including IBM researchcenters and Fine Arts Museums of San Francisco).