RI-Small: Discovery, Modeling and Recognition of Objects in Image Sets

RI-Small：图像集中对象的发现、建模和识别

• 批准号: 0812188
• 负责人: Narendra Ahuja
• 金额: $ 38万
• 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: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812188&HistoricalAwards=false
• 关键词: RI; Small; Discovery; Modeling; Recognition

This project is about automated, visual object recognition. It is aimed at a computational approach which has two parts. First, it learns whether a given set of previously unseen images, say supplied by a user, contains any dominant themes, namely, subimages, that occur frequently and look similar. Such themes, and the associated subimages, are called categories and objects, respectively. Second, given a set of categories automatically inferred during the aforementioned training, and a new, test image, the approach recognizes all occurrences in the image of objects belonging to any 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 need human supervision. The subimages defining a category can be small or large, simple or complex. It is reasonable to expect that low-complexity categories, e.g., containing small/few/simple subimages are more common in real-world images. For example, the simple category of elongated shapes occurs as a part of legged animals, stools and scissors. More complex categories consist of large/many/complicated regions and are less common. Simple categories, e.g., the ``leg'' are thus shared by more complex ones, e.g., all legged animals, and, in turn, ``leg'' is an articulated combination of the category of elongated shapes (limbs). Therefore, category representation can be made easier by expressing it as a configuration of simpler categories, instead of subimages directly, thus yielding a hierarchical, subpart model. Accordingly, the proposed approach learns and recognizes categories as image hierarchies. The use of hierarchical embedding of regions as the defining image features results in several advantages the proposed approach offers over existing other methods which mostly use local features: (1) The proposed approach requires no supervision, e.g., labeling or segmenting of training images, or other input parameters from the user. (2) It simultaneously provides category detection and high-accuracy segmentation. (3) Training is feasible with very few examples, and not all training images must contain objects from the categories. (4) The use of hierarchical models makes explicit the relationship of a specific category to other categories of similar, lower and higher complexities; it also serves as a semantic explanation of why a category is detected when detected. Expected major contributions of the work include computational formulations of: (1) Accurate extraction of image regions; (2) Image representation by connected segmentation tree; (3) Robust image matching amidst structural noise in images; (4) Unsupervised extraction of hierarchical category models; (5) Efficient recognition of a large number of categories; (6) Unsupervised estimation of the relevance weights of subcategory detections to category recognition, and (7) Generalization of the proposed approach to extraction of texture elements, as an example of how the proposed work may impact other challenging vision problems involving hierarchy.The progress made on this project can be seen at the website: http://vision.ai.uiuc.edu/ahuja.html

这个项目是关于自动化的，视觉对象识别。它针对的是一种由两部分组成的计算方法。首先，它了解给定的一组以前未见过的图像，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要主题，即子图像。这样的主题和关联子图像分别称为类别和对象。其次，给定在上述训练期间自动推断的一组类别和新的测试图像，该方法识别属于任何学习类别的对象在图像中的所有出现。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。定义类别的子图可以是小的或大的、简单的或复杂的。可以合理地预期，低复杂性类别，例如，包含小/少/简单子图像的类别在真实世界图像中更为常见。例如，细长形状的简单类别作为腿部动物、凳子和剪刀的一部分出现。更复杂的类别由大/多/复杂的区域组成，不太常见。因此，更复杂的类别，例如，所有有腿的动物都共享简单的类别，例如‘腿’，而‘腿’又是细长形状(肢体)类别的关节组合。因此，通过将类别表示表示为更简单的类别的配置，而不是直接对子图像，可以使类别表示变得更容易，从而产生分层的子部分模型。因此，所提出的方法将类别学习和识别为图像层次。与现有的主要使用局部特征的方法相比，分层嵌入区域作为定义图像特征的方法具有几个优点：(1)该方法不需要监督，例如，训练图像的标记或分割，或来自用户的其他输入参数。(2)同时提供类别检测和高精度的切分。(3)训练是可行的，只需要很少的例子，并且不是所有的训练图像都必须包含类别中的对象。(4)层次模型的使用使特定类别与其他具有相似、较低和较高复杂性的类别之间的关系变得清晰；它也可以作为一种语义解释，解释为什么当检测到一个类别时会被检测到。预期的主要贡献包括：(1)图像区域的准确提取；(2)用连通分割树表示图像；(3)在图像中的结构性噪声中进行稳健的图像匹配；(4)分层类别模型的无监督提取；(5)大量类别的有效识别；(6)子类别检测与类别识别的相关性权重的无监督估计，以及(7)对所提出的提取纹理元素的方法的推广，作为所提出的工作可能如何影响涉及层次的其他具有挑战性的视觉问题的例子。该项目的进展可在网站上看到：http://vision.ai.uiuc.edu/ahuja.html