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

自动识别图像中的人、地点、物体，特别是物体类别是计算机视觉中的一个中心和持续的挑战。这个项目使用低级图像特征来学习中间表示法来解决这个问题，在中间表示法中，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。这项工作从三个领域演示了这种方法：面部、植物种类和建筑。在每个领域，该项目开发了获取视觉属性词汇表、训练属性检测器和建立合成模型以自动标记图像中的属性的技术。该项目在视觉属性的使用方面做出了四项基本贡献。1)它正在开发新的方法，通过这些方法，自动系统和人类可以交互来选择适合领域的属性词汇并标记大型图像集合。2)它正在开发组合模型，以捕获属性之间的依赖关系。这提供了更准确的属性检测，并能够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、可本地化的属性，以捕捉对象部分和地标之间的几何关系。4)该项目正在设计结合属性来识别对象、在图像海量集合中搜索并自动标注图像数据库的算法。这项研究不仅产生了标记图像的大型数据集，这将有助于催化新的研究，而且还展示了在人脸、植物和建筑等专业领域中分析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.