Perceptual Grouping and Shape Abstraction

目标分类仍然是计算机视觉面临的最重要的挑战之一。当在图像中搜索特定的目标对象时(对象检测问题)，目标在帮助分割和解释图像特征之前提供较强的形状。但是对于来自大型数据库的更一般的对象分类任务，没有这样的目标对象可用。取而代之的是，我们必须依赖于一套独立于对象的、中层的形状先验，它们反映了我们世界的规则--100年前格式塔心理学家确定的规则。这个问题可以表述如下。在给定一幅杂乱场景的图像并且没有场景内容的先验知识的情况下，我们的首要任务是将属于同一对象的图像特征组合在一起。这是知觉分组的经典问题，近年来由于目标检测问题的突出，这一问题在很大程度上被视觉界忽视了，在该问题中，较强的对象级先验包含较弱的中级先验。因此，该建议的主要目标之一是对这些中层形状先验(例如对称性、连续性、交汇点和闭合)进行建模，并开发能够检测这些规律性并使用它们将场景分割成因果相关的图像特征集合的算法，每个图像特征集合对应于不同的对象。*最终，我们希望识别这些要素集合所描述的对象。但这就是处理类内差异很大的类别时的问题所在。组成分类模型的特征的粒度很粗，而组成集合的图像特征的粒度很细。这种差异通常被称为计算机视觉中的语义差异。因此，我们必须抽象(或正规化)局部图像特征组，以便将它们提升到数据库中组成模型的粗略、原型特征的级别。这也提出了许多关键的研究问题，也在本提案中涉及：1)如何对对象的抽象形状进行建模？2)什么是不变部分，其检测可以帮助我们确定我们可能正在查看的对象？(称为对象索引的问题)；3)我们如何使用抽象部件词汇表的知识来驱动抽象过程，以帮助我们从图像中恢复这些部件？4)这些部件应该是2-D还是3-D？*我们的研究计划解决了两个重要且密切相关的问题：感知分组和形状抽象。在不了解场景内容的情况下，从图像中恢复一组抽象的、基于部分的图像特征的能力产生了一种强大的索引机制，该机制可以将大型数据库削减到少数有希望的候选对象，而这些候选对象又可以提供强大的、自上而下的先验来分割和检测场景中的对象。**

Object categorization continues to be one of the most important challenges facing computer vision. When a particular target object is searched for in an image (the object detection problem), the target provides a strong shape prior to help segment and interpret the image features. But for the more general task of object categorization from a large database, no such target object is available. Instead, we must rely on a set of object-independent, mid-level shape priors that reflect the regularities of our world -- regularities identified by the Gestalt psychologists 100 years ago. The problem can be formulated as follows. Given an image of a cluttered scene and no a priori knowledge of scene content, our first task is to group together image features that belong to the same object. This is the classical problem of perceptual grouping, which has been largely ignored by the vision community in recent years due to the prominence of the object detection problem, in which stronger object-level priors subsume weaker mid-level priors. One of the primary objectives of this proposal is therefore to model these mid-level shape priors, such as symmetry, continuity, junctions, and closure, and to develop algorithms that can detect these regularities and use them to segment a scene into causally-related collections of image features, each corresponding to a different object. ******Ultimately, we would like to recognize the objects that these feature collections depict. But herein lies the problem when dealing with categories that exhibit high within-class variation. The granularity of the features comprising a categorical model is very coarse, whereas the granularity of the image features comprising a collection is very fine. This disparity is often referred to as the semantic gap in computer vision. We must therefore abstract (or regularize) the groups of local image features in order to "lift" them up to the level of the coarse, prototypical features that make up the models in the database. This raises many critical research questions also addressed in this proposal: 1) how do we model the abstract shape of an object?; 2) what are the invariant parts whose detection can help us determine what object we may be looking at? (a problem known as object indexing); 3) how can we use knowledge of a vocabulary of abstract parts to drive the abstraction process that will help us recover such parts from an image?; and 4) should these parts be 2-D or 3-D? ******Our research program addresses the two important and closely related problems of perceptual grouping and shape abstraction on multiple fronts. Without any knowledge of scene content, the ability to recover from an image a set of abstract, part-based image features yields a powerful indexing mechanism that can prune a large database down to a small number of promising candidates which, in turn, can provide strong, top-down priors to segment and detect the objects in a scene.**