RI: Small: Collaborative Research: Visual Attributes for Identification and Search in Images

RI：小型：协作研究：图像中识别和搜索的视觉属性

• 批准号: 1116631
• 负责人: David Jacobs
• 金额: $ 25万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116631&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Visual

The automatic identification in images of people, places, objects, and especially object categories is a central and ongoing challenge within computer vision. This project addresses this problem using low-level image features to learn intermediate representations, ones in which objects in images are labeled with an extensive list of highly descriptive visual attributes. This work demonstrates this approach in three domains: faces, plant species, and architecture. In each domain, it develops techniques for deriving visual attribute vocabularies, training attribute detectors, and building compositional models to automatically label attributes in images.The project is making four fundamental contributions to the use of visual attributes. 1) It is developing new methods by which automatic systems and humans can interact to select domain-appropriate attribute vocabularies and label large image collections. 2) It is developing compositional models that capture dependencies between attributes. This provides more accurate attribute detection and enables inference of global properties of objects. 3) Using compositional models, the project is developing new, localizable attributes that capture the geometric relations between object parts and landmarks. 4) The project is designing algorithms that combine attributes to identify objects, search through image vast collections, and automatically annotate image databases.Not only is this research generating large datasets of labeled images that should help catalyze new research, it is also demonstrating the feasibility of new systems for analyzing images in specialized domains such as faces, plants, and architecture. For example, the project develops new software applications for analyzing and searching images of faces as well as free mobile apps for plant species identification.

人物，地点，对象，尤其是对象类别的图像中的自动标识是计算机视觉中的核心和持续挑战。 该项目使用低级图像功能来解决此问题，以学习中间表示，其中图像中的对象被标记为高度描述性的视觉属性列表。 这项工作在三个领域中证明了这种方法：面部，植物物种和建筑。 在每个域中，它都会开发出用于得出视觉属性词汇，训练属性检测器和构建组成模型以自动在图像中标记属性的技术。该项目为视觉属性的使用做出了四个基本贡献。 1）它正在开发新方法，通过这些方法，自动系统和人类可以通过这些方法进行交互以选择适合域的属性词汇并标记大图像集。 2）它正在开发构成模型，以捕获属性之间的依赖性。 这提供了更准确的属性检测，并可以推断对象的全局属性。 3）使用组成模型，该项目正在开发新的可本质属性，以捕获对象零件和地标之间的几何关系。 4）该项目是设计算法，这些算法结合属性以识别对象，搜索图像庞大的集合并自动注释图像数据库。不仅，这项研究生成了大量的标记图像数据集，这些图像应有助于催化新的研究，它还证明了在诸如面部和建筑之类的特定域中分析新系统的可行性，例如，诸如工厂，建筑和建筑。例如，该项目开发了用于分析和搜索面部图像以及免费移动应用程序的新软件应用程序，以获取植物物种识别。