EAGER: Automated High Speed Object Category Modeling and Model Based Recognition, Segmentation, Clustering, and Classification

EAGER：自动化高速对象类别建模和基于模型的识别、分割、聚类和分类

• 批准号: 1144227
• 负责人: Narendra Ahuja
• 金额: $ 26.63万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1144227&HistoricalAwards=false
• 关键词: EAGER; Automated; Speed; Object; Category

This project explores new directions to solving the following problem. Given an image, determine whether and where specific objects, or objects from a specific category, appear in the image. Visual category is defined as earlier, namely, as a collection of objects which share characteristic features that are visually similar, and occur in similar configurations. The visual nature of objects sought is communicated through (training) data containing them, and estimated using machine learning. The approach consists of two main parts. First, it learns whether a given set of previously unseen images (including videos), say supplied by a user, contains any dominant themes, namely, subimages, that occur frequently and look similar. Second, given a set of categories automatically inferred during training and a new test image, the approach recognizes all occurrences in the image of the learned categories. It delineates each such object in the image, and labels it with its category name. Both learning and subsequent recognition do not require human supervision. The approach learns and recognizes categories as image hierarchies. The impact of the project includes accurate high-speed extraction of image regions, image representation by connected segmentation tree, robust image matching, unsupervised extraction of hierarchical category models, efficient recognition of a large number of categories, unsupervised estimation of perceptually salient, relevance weights of subcategory detections to category recognition, and generalization of the proposed approach to extraction of texture elements. More broadly, the proposed approach is useful for applications in search engines, surveillance, video analytics, monitoring and data mining.

这个项目探索了解决以下问题的新方向。给定一幅图像，确定特定对象或来自特定类别的对象是否以及在图像中出现的位置。视觉类别的定义如前所述，即，共享视觉上相似且以相似配置出现的特征特征的对象的集合。寻找的物体的视觉特性通过包含它们的(训练)数据来传达，并使用机器学习来估计。该方法由两个主要部分组成。首先，它学习给定的一组先前未见过的图像(包括视频)，例如由用户提供的，是否包含任何频繁出现且看起来相似的主导主题，即子图像。其次，在给定训练期间自动推断的一组类别和新的测试图像的情况下，该方法识别学习类别的图像中的所有出现。它描绘了图像中的每个这样的对象，并使用其类别名称对其进行标记。学习和随后的认可都不需要人的监督。该方法将类别学习和识别为图像层次结构。该项目的影响包括图像区域的准确高速提取、图像的连通分割树表示、稳健的图像匹配、层次化类别模型的无监督提取、大量类别的有效识别、感知显著程度的无监督估计、子类别检测与类别识别的相关性权重、以及所提出的纹理元素提取方法的推广。更广泛地说，建议的方法在搜索引擎、监控、视频分析、监控和数据挖掘中的应用是有用的。