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

EAGER：用于对象识别和分割的对象片段和对象类别的度量空间嵌入

• 批准号: 0957045
• 负责人: Benjamin Kimia
• 金额: $ 8万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0957045&HistoricalAwards=false
• 关键词: EAGER; Metric; Space; Embedding; Object

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used in appearance-based recognition, such as formation of visual words from k-means, vocabulary trees, etc.are no longer applicable.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

近年来，基于形状的增强物体识别方法的发展利用了局部形状特征来类比外观特征。然而，过去对形状的研究表明，形状比局部特征的综合要丰富得多。相反，形状是非常高维的，并且无法在合理维数的欧几里得空间中进行全局嵌入。因此，基于外观的识别中使用的欧几里得空间概念，如从k-means形成视觉词、词汇树等不再适用。该项目正在开发类似的概念，以便在形状度量空间的背景下对大量类别进行有效的索引。这些概念正在一个综合的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，使用一种新颖的形状语言的自上而下的方法已经超过了ETHZ数据集的技术水平。然而，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，基于片段的自底向上方法显示了一类Weizmann Horse数据库的最先进性能。要扩展使用更多的类别，就需要对对象空间和对象碎片空间进行组织。该项目旨在使用接近图捕获两个空间的度量结构，然后用于有效的索引。这两项发展将共同促成一种综合方法，其中自底向上方法缩小了类别的选择范围，然后由自顶向下方法进行检查。更广泛的影响包括用于国防应用的空中跟踪和车辆识别，脊柱x射线透视图像的分割，以及数据库（例如商标）的索引。