RI: Small: A Generic Mid-Level Representation as Object Part Hypotheses for Scalable Object Category Recognition

该项目开发了一种基于形状和外观的基于片段的图像中间层表示，其中形状起主要作用。该表示可以以自下而上、独立于类别的方式来填充，同时可以由依赖于类别的顶层过程高效地访问。通过考虑形状和外观以及基于区域和基于边界的提示，通过标准和新颖的感知操作组合地形成可替换的图像片段，解决了在生成对象部分假设时的固有模糊性。碎片数量的指数增长在避免重复的最佳优先图表示下进行管理，从而在大量碎片中产生足够数量的诊断可识别对象部件。该项目还探索了通过邻近图和几何索引结构将这些片段嵌入到度量相似空间中，以实现高效的最近邻搜索。最终结果是表示空间和用于可伸缩的对数对象类别识别的索引。这项工作的一个关键方面是，范畴本身也被表示在一个层次相似空间中，这在计算上实现了类似于Rosch？S基本级别分类的思想。这一活动的更广泛影响跨越了大量应用程序：任何受益于可伸缩对象识别的应用程序，例如索引到数据库中，例如在商标、工程图纸和计算机生成的图形数据库中进行搜索，基于内容的网络搜索，车辆的空中跟踪和识别，自动动物行为分析等。

This project develops a fragment-based intermediate-level representation for images based on shape and appearance, with shape playing the primary role. The representation can be populated in a bottom-up, category-independent fashion, which at the same time can be efficiently accessible by top-level, category dependent processes. The inherent ambiguity in generating object part hypotheses is resolved by combinatorially forming alternative image fragments by standard as well as novel perceptual operations, taking into account both shape and appearance, and both region-based and boundary-based cues. The exponential growth of the number of fragments is managed under a best-first graph representation that avoids duplication, leading to a sufficient number of diagnostic recognizable object parts among a vast pool of fragments. The project also explores an embedding of these fragments in a metric similarity space via proximity graphs and a geometric index structures for efficient nearest neighbor search. The final outcome is a representation space and an index for scalable, logarithmic object category recognition. A key aspect of this work is that categories themselves are also represented in a hierarchical similarity space, and this computationally implements ideas akin to Rosch?s basic level categorization. The broader impacts of this activity spans a vast number of applications: any application which benefits from scalable object recognition such as indexing into databases, e.g., searching in a database of trademarks, engineering drawings and computer generated graphics, content-based web search, aerial tracking and recognition of vehicles, automated animal behavior analysis, and many others.