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

这个项目是关于自动的，视觉对象识别。它的目标是一种由两部分组成的计算方法。首先，它学习一组给定的以前未见过的图像，比如由用户提供的图像，是否包含任何经常出现且看起来相似的主导主题，即子图像。这些主题和相关的子图像分别被称为类别和对象。其次，给定一组在上述训练过程中自动推断的类别，以及一个新的测试图像，该方法识别图像中属于任何学习类别的物体的所有出现。它在图像中描绘每个这样的对象，并用其类别名称标记它。学习和随后的识别都不需要人类的监督。定义一个类别的子图像可以是大的或小的，简单的或复杂的。我们有理由期望低复杂度的分类，例如，包含小/少/简单子图像的分类在真实世界的图像中更常见。例如，细长形状的简单类别出现在有腿的动物，凳子和剪刀的一部分。更复杂的类别由大/多/复杂的区域组成，不太常见。简单的类别，例如“腿”，因此被更复杂的类别所共享，例如所有有腿的动物，反过来，“腿”是细长形状（四肢）类别的清晰组合。因此，通过将类别表示表示为更简单类别的配置，而不是直接将其表示为子图像，可以使类别表示更容易，从而产生分层的子部件模型。因此，所提出的方法学习和识别类别作为图像层次结构。使用区域的分层嵌入作为图像特征的定义，与现有的其他主要使用局部特征的方法相比，该方法具有以下几个优点：(1)该方法不需要监督，例如，训练图像的标记或分割，或用户的其他输入参数。(2)同时提供类别检测和高精度分割。(3)训练在样本很少的情况下是可行的，并不是所有的训练图像都必须包含类别中的对象。(4)层次模型的使用明确了特定类别与其他相似、较低和较高复杂性类别之间的关系；它还可以作为一种语义解释，说明为什么在检测到类别时检测到类别。预期的主要贡献包括以下方面的计算公式：(1)准确提取图像区域；(2)连通分割树图像表示；(3)图像结构噪声下的鲁棒图像匹配；(4)层次分类模型的无监督提取；(5)高效识别大量类别；(6)对子类别检测与类别识别的相关权值进行无监督估计；(7)将所提出的方法推广到纹理元素的提取中，作为所提出的工作如何影响涉及层次的其他具有挑战性的视觉问题的一个例子。该项目的进展可以在网站上看到：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