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

在图像中自动识别人、地点、物体，特别是物体类别是计算机视觉中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。 这项工作证明了这种方法在三个领域：面孔，植物物种和建筑。 在每一个领域，它都开发了用于导出视觉属性词汇表、训练属性检测器和构建合成模型以自动标记图像中的属性的技术。1)它正在开发新的方法，通过这种方法，自动系统和人类可以交互选择适合领域的属性词汇表和标记大型图像集合。2)它正在开发捕捉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，并能够推断对象的全局属性。 3)使用组合模型，该项目正在开发新的，可本地化的属性，捕捉对象部分和地标之间的几何关系。 4)该项目设计的算法结合联合收割机属性来识别物体，搜索大量图像，并自动注释图像数据库。这项研究不仅生成了大量的标记图像数据集，有助于促进新的研究，还展示了新系统在面部、植物和建筑等专业领域分析图像的可行性。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的应用程序。

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.