Statistical Modeling and Learning in Vision

视觉中的统计建模和学习

• 批准号: 1007889
• 负责人: Yingnian Wu
• 金额: $ 30万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1007889&HistoricalAwards=false
• 关键词: Statistical; Modeling; Learning; Vision

Finding statistical models that capture the regularities and variabilities of the bewildering varieties of visual patterns in natural scenes is at the heart of understanding the mystery of vision. Continuing the pattern-theoretical approach pioneered by Grenander and advocated by Mumford, and building on the active basis model that the PIs have recently developed, the PIs propose research projects to further develop statistical models as well as associated learning and inference algorithms for vision. The active basis model is a mathematical representation of deformable templates of object patterns. Each template is a sparse composition of selected Gabor wavelet elements that are allowed to perturb their locations and orientations. The template can be learned from training images by a shared sketch algorithm. The learned template can then be used to recognize objects from testing images using a cortex-like architecture of sum-max maps. The proposed research develops hierarchical compositional models with active basis models as building blocks or part-templates. The proposed research studies unsupervised learning of dictionaries of active basis templates from natural images or images of objects from multiple categories and viewpoints. The proposed research also studies a shape script model where the part-templates are designed elementary geometric shapes that are represented by the active basis models. Moreover, the proposed research compares generative and discriminative approaches to learning, using active basis model as an example of generative model. In addition, the proposed research extends the active basis model by coupling wavelet sparse coding for shape patterns and Markov random fields for texture patterns.Biological visual cortex can learn and recognize huge number of visual patterns in its environment effortlessly. One may consider the visual cortex as an extremely sophisticated statistical model equipped with extremely efficient and robust learning and inference algorithms. What this model looks like and how it learns from its visual environment is still a deep mystery. The proposed research has the potential to contribute to advancing our understanding of this issue. It also leads to concrete models and algorithms that can be used for learning and recognizing a wide variety of object patterns.

寻找能够捕捉自然场景中各种令人眼花缭乱的视觉模式的规律性和变化性的统计模型是理解视觉之谜的核心。继续由 Grenander 开创并由 Mumford 倡导的模式理论方法，并以 PI 最近开发的主动基础模型为基础，PI 提出了研究项目，以进一步开发统计模型以及相关的视觉学习和推理算法。主动基础模型是对象图案的可变形模板的数学表示。每个模板都是选定的 Gabor 小波元素的稀疏组合，这些元素可以扰乱其位置和方向。可以通过共享草图算法从训练图像中学习模板。然后，学习的模板可用于使用最大和图的类皮层架构来识别测试图像中的对象。拟议的研究开发了分层组合模型，以主动基础模型作为构建块或部分模板。拟议的研究研究了来自多个类别和观点的自然图像或物体图像的主动基础模板词典的无监督学习。拟议的研究还研究了形状脚本模型，其中零件模板被设计为由活动基础模型表示的基本几何形状。此外，所提出的研究比较了生成式和判别式学习方法，并使用主动基础模型作为生成模型的示例。此外，该研究通过耦合形状模式的小波稀疏编码和纹理模式的马尔可夫随机场来扩展主动基础模型。生物视觉皮层可以毫不费力地学习和识别其环境中的大量视觉模式。人们可能会认为视觉皮层是一种极其复杂的统计模型，配备了极其高效和强大的学习和推理算法。这个模型是什么样子以及它如何从视觉环境中学习仍然是一个很深的谜团。拟议的研究有可能有助于增进我们对这个问题的理解。它还产生了可用于学习和识别各种对象模式的具体模型和算法。