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

这个项目是关于自动化，视觉对象识别。它的目的是计算方法，其中有两个部分。首先，它会了解给定的一组以前未见过的图像（例如用户提供的图像）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。这样的主题，以及相关的子图像，分别被称为类别和对象。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，该方法识别图像中属于任何学习类别的对象的所有出现。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的监督。定义类别的子图像可以是小的或大的，简单的或复杂的。可以合理地预期，低复杂度的类别，例如，包含小的/很少的/简单的子图像在真实世界的图像中更常见。例如，细长形状的简单类别作为有腿动物、凳子和剪刀的一部分出现。更复杂的类别由大/许多/复杂的区域组成，并且不太常见。简单类别，例如，因此，“腿”被更复杂的腿共享，例如，所有有腿的动物，反过来，“腿”是细长形状（四肢）类别的铰接组合。因此，类别表示可以通过将其表示为更简单类别的配置而不是直接的子图像来变得更容易，从而产生分层的子部分模型。因此，所提出的方法学习和识别类别的图像层次结构。使用区域的分层嵌入作为定义图像特征导致所提出的方法相对于主要使用局部特征的现有其他方法提供了几个优点：（1）所提出的方法不需要监督，例如，标记或分割训练图像或来自用户的其他输入参数。(2)它同时提供类别检测和高精度分割。(3)训练是可行的，只有很少的例子，并不是所有的训练图像都必须包含来自类别的对象。(4)层次模型的使用明确了特定类别与其他类似类别的关系，更低和更高的复杂性;它也可以作为一个语义解释，为什么一个类别被检测时检测到。预期的主要贡献的工作包括计算公式：（1）准确提取的图像区域;（2）图像表示的连接分割树;（3）鲁棒的图像匹配中的结构噪声;（4）无监督提取的层次类别模型;（5）有效地识别大量的类别;（6）子类别检测到类别识别的相关权重的无监督估计，以及（7）所提出的纹理元素提取方法的推广，作为一个例子，说明拟议的工作如何影响其他涉及层次结构的具有挑战性的视觉问题。这个项目的进展情况可以在网站上看到：http://vision.ai.uiuc.edu/ahuja.html

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