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

人、地点、物体，特别是物体类别图像的自动识别是计算机视觉领域的一个核心和持续的挑战。该项目使用低级图像特征来学习中间表示来解决这个问题，在中间表示中，图像中的对象被标记为具有高度描述性的视觉属性的广泛列表。这项工作在三个领域展示了这种方法：面孔、植物物种和建筑。在每个领域中，它开发了派生视觉属性词汇表、训练属性检测器和构建组合模型以自动标记图像中的属性的技术。该项目对视觉属性的使用做出了四个基本贡献。1)它正在开发新的方法，通过自动系统和人类交互来选择适合领域的属性词汇表和标记大型图像集合。2)正在开发捕获属性之间依赖关系的组合模型。这提供了更准确的属性检测，并支持对对象的全局属性进行推断。3)使用组合模型，该项目正在开发新的、可定位的属性，以捕获物体部分和地标之间的几何关系。4)该项目正在设计结合属性识别对象的算法，在图像海量集合中搜索，并自动注释图像数据库。这项研究不仅产生了大量的标记图像数据集，这应该有助于催化新的研究，它也证明了分析特定领域（如人脸、植物和建筑）图像的新系统的可行性。例如，该项目开发了用于分析和搜索人脸图像的新软件应用程序，以及用于植物物种识别的免费移动应用程序。

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.