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

这个项目探索了解决以下问题的新方向。给定一幅图像，确定特定对象或特定类别的对象是否出现在图像中以及出现在图像中的位置。视觉类别的定义与前面一样，即，作为共享视觉上相似的特征的对象的集合，并以相似的配置出现。所寻求的对象的视觉性质通过包含它们的（训练）数据进行传达，并使用机器学习进行估计。该方法包括两个主要部分。首先，它会学习用户提供的一组给定的以前看不见的图像（包括视频）是否包含任何主导主题，即经常出现并且看起来相似的子图像。第二，给定一组在训练过程中自动推断的类别和一个新的测试图像，该方法识别出所学习的类别的图像中的所有事件。它描绘了图像中的每个这样的对象，并用其类别名称标记它。学习和随后的识别都不需要人类监督。该方法将类别学习和识别为图像层次结构。该项目的影响包括准确的高速提取图像区域，图像表示连接分割树，鲁棒的图像匹配，无监督提取的层次类别模型，有效识别大量的类别，无监督估计的感知显着性，相关权重的子类检测类别识别，和泛化的纹理元素提取的方法。更广泛地说，所提出的方法对于搜索引擎、监控、视频分析、监控和数据挖掘中的应用是有用的。

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.