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

RI：小：作为可扩展对象类别识别的对象部分假设的通用中级表示

• 批准号: 1319914
• 负责人: Benjamin Kimia
• 金额: $ 45万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319914&HistoricalAwards=false
• 关键词: RI; Small; Generic; Mid; Level

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.

这个项目开发了一个基于片段的中间级表示的图像的基础上的形状和外观，与形状扮演的主要角色。该表示可以以自下而上、类别独立的方式填充，同时可以由顶层、类别相关的过程有效地访问。在生成对象部分假设的固有的模糊性是通过组合形成替代图像片段的标准以及新颖的感知操作，同时考虑到形状和外观，以及基于区域和基于边界的线索来解决。在避免重复的最佳优先图表示下管理片段数量的指数增长，从而在大量片段池中产生足够数量的诊断可识别对象部分。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度量相似性空间中，以实现高效的最近邻搜索。最终的结果是一个表示空间和可扩展的，对数对象类别识别的索引。这项工作的一个关键方面是，类别本身也表示在一个层次的相似性空间，这计算实现类似于Rosch？的基本层次分类。这一活动的更广泛的影响跨越了大量的应用程序：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，在商标、工程图纸和计算机生成图形的数据库中搜索，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，以及许多其他。