EAGER: A Metric Space Embedding of Object Fragments and Object Categories for Object Recognition and Segmentation

最近的事态发展，在增强外观为基础的方法，以对象识别的形状使用局部形状特征类似的外观特征。然而，过去对形状的研究表明，形状比局部特征的集合要丰富得多。相反，形状是非常高维的，并且无法在合理维度的欧几里得空间中进行全局嵌入。因此，在基于外观的识别中使用的欧几里德空间概念，例如从k均值、词汇树等形成视觉单词，不再适用。该项目正在开发类似的概念，用于在形状度量空间的背景下对大量类别进行有效的索引。这些概念正在集成自下而上和自上而下的对象识别和分割框架的背景下进行研究。首先，使用一种新的形状语言的自上而下的方法已经超过了ETHZ数据集中的最新技术水平。但是，原型形状是手动选择的。该项目旨在使用结构平均的概念来自动形成原型形状。第二，基于片段的自底向上的方法已经显示了一个类别的魏茨曼马数据库的最先进的性能。扩展到使用更多的类别需要组织对象空间和对象片段的空间。该项目旨在使用邻近图捕获两个空间的度量结构，然后将其用于高效索引。 这两项发展将共同实现一种综合方法，其中自下而上的方法缩小了类别的选择范围，然后由自上而下的方法进行检查。更广泛的影响包括空中跟踪和识别国防应用的车辆，脊柱X射线透视图像的分割，以及数据库的索引，例如，商标的

Recent developments in augmenting appearance-based approaches to object recognition with shape have used local shape features in analogy to appearance features. However, past work on shape has shown that shape is much richer than a conglomerate of local features. Rather, shape is very high-dimensional and defies global embedding in a reasonably-dimensioned Euclidean space. Thus, Euclidean space concepts used in appearance-based recognition, such as formation of visual words from k-means, vocabulary trees, etc.are no longer applicable. This project is developing analogous concepts for efficient indexing with a large number of categories in the context of a metric space for shape.These concepts are being investigated in the context of an integrated bottom-up and top-down object recognition and segmentation framework. First, a top-down approach using a novel language for shape has already exceeded the state of the art in the ETHZ dataset. However, the prototypical shapes are manually selected. The project aims to use the concept of structural averaging to automatically form prototypical shapes. Second, a fragment-based bottom-up approach has shown state of the art performance for a one-category Weizmann Horse database. An extension to the use of more categories requires an organization of the object space and the space of object fragments. The project aims to capture the metric structure of both spaces using a proximity graph, which is then used for efficient indexing. These two developments will together enable an integrated approach where bottom-up methods narrow a selection of categories which are then examined by the top-down approach.Broader impacts include aerial tracking and recognition of vehicles for defense applications, segmentation of X-ray fluoroscopic images of the spine, and indexing into databases, e.g., trademarks.