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

在增强基于外观的具有形状的对象识别方法方面的最新发展已经使用局部形状特征来类似于外观特征。然而，过去对形状的研究表明，形状比当地特色的集合体丰富得多。相反，形状是非常高维的，不能将全局嵌入到合理维度的欧几里德空间中。因此，在基于外观的识别中使用的欧几里得空间概念，例如从k-均值、词汇树等形成视觉单词等不再适用。该项目正在制定类似的概念，以便在形状度量空间的背景下使用大量类别进行有效的索引。正在综合的自下而上和自上而下的目标识别和分割框架的背景下研究这些概念。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数据集中的最先进水平。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。其次，基于片段的自下而上方法展示了单类别Weizmann Horse数据库的最新性能。对更多类别的使用的扩展需要组织对象空间和对象碎片的空间。该项目旨在使用接近度图捕捉这两个空间的公制结构，然后将其用于有效的索引。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将缩小类别的选择范围，然后由自上而下的方法进行检查。广泛的影响包括用于国防应用的车辆的空中跟踪和识别，脊柱X射线透视图像的分割，以及到数据库中的索引，例如商标。

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used in appearance-based recognition, such as formation of visual words from k-means, vocabulary trees, etc.are no longer applicable.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.