ITR - (ASE+NHS) - (dmc+int): Triage and the Automated Annotation of Large Image Data Sets

Johns Hopkins University, Baltimore MD

Investigators

Abstract

Proposal: 0427223 Principal Investigators: Donald Geman, Yali Amit, Stuart Geman, and Laurent Younes Institutions: Johns Hopkins U, U of Chicago, Brown U, and Johns Hopkins U Proposal Title: ITR-\(ASE+NHS)\-\(dmc+int)\: Triage and Automated Annotation of large Image Data Sets ABSTRACT The long-term goal is a computational and mathematical framework for progressive annotation of large image databases at a semantic level. The proposed research fuses two powerful paradigms, coarse-to-fine indexing and syntactic scene parsing, into one model and computational strategy in order to provide a less-to-more detailed annotation of a scene as a function of available computing cycles. Coarse, and likely flawed, interpretations emerge at the early stages of processing, followed by finer and more accurate ones. The fusion between coarse-to-fine and syntactic models allows one to gain the best of both: a nearly optimal computational engine for detecting candidate constituents embedded in a full semantic and syntactic scene analysis. Large image data sets are ubiquitous. Sources include medicine, manufacturing, astrophysics, molecular biology, defense and intelligence. In general, these resources are useful only in proportion to our ability to access selected semantic categories, such as ``includes people'' or ``contains a frontal lobe mass''. There is then a great need for automated annotation, whereby computer programs would produce a ``meta'' data structure, partially describing the contents and context of each image. Progress in automated scene annotation will have an immediate impact on a broad range of scientific disciplines, including medicine and surveillance .