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

提案：0427223主要研究者：Donald Geman、Yali Amit、Stuart Geman和Laurent Younes研究机构：约翰霍普金斯大学、芝加哥大学、布朗大学和约翰霍普金斯大学提案标题：ITR-\（ASE+NHS）\-\（dmc+int）\：大型图像数据集的分类和自动注释摘要长期目标是在语义层面上为大型图像数据库的渐进注释提供一个计算和数学框架。 拟议的研究融合了两个强大的范例，粗到细的索引和句法场景解析，到一个模型和计算策略，以提供一个少到更详细的注释的场景作为可用的计算周期的函数。 粗糙的，可能有缺陷的，解释出现在处理的早期阶段，其次是更精细和更准确的。 由粗到细和句法模型之间的融合使人们能够获得两者的最佳效果：一个近乎最佳的计算引擎，用于检测嵌入在完整语义和句法场景分析中的候选成分。 来源包括医学，制造业，天体物理学，分子生物学，国防和情报。 一般来说，这些资源的有用程度与我们获取特定语义类别的能力成正比，比如“包括人”或“包含额叶肿块”。 因此，非常需要自动注释，计算机程序将产生一个“元”数据结构，部分描述每个图像的内容和上下文。 自动化场景注释的进展将对包括医学和监控在内的广泛科学学科产生直接影响。

Proposal: 0427223Principal Investigators: Donald Geman, Yali Amit, Stuart Geman, and Laurent YounesInstitutions: Johns Hopkins U, U of Chicago, Brown U, and Johns Hopkins UProposal Title: ITR-\(ASE+NHS)\-\(dmc+int)\: Triage and Automated Annotation of large Image Data SetsABSTRACTThe 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 .