Visual Learning in Context

情境中的视觉学习

• 批准号: 0534897
• 负责人: Carlo Tomasi
• 金额: $ 35.85万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-15 至 2009-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0534897&HistoricalAwards=false
• 关键词: Visual; Learning; Context

Recent successes in computer vision, machine learning, and combinatorial optimization are leveraged to tackle once more the image interpretation problem as defined in the early days of computer vision. Interpretation is cast as a problem of simultaneous image segmentation and region classification: Given an image and a list of class labels, the goal is to compute the most probable image segmentation and labeling, one label per segment. This is learning in context in that learning techniques are used to recognize several objects in the context of complex, cluttered imagesA manual image labeling method is proposed that enlists the help of both web surfers and the students in a junior-high school and a high school to tackle this labor intensive task, while at the same time exposing young pupils to computer vision research.The proposed work has intellectual merit of relevance to the fields of computer vision, artificial intelligence, and cognition in general. In particular, the notion of defining ``words for pictures'' that this proposal offers may establish a new, fruitful bridge to text retrieval research and widen the discourse computer vision has been entertaining with other areas of science.The understanding of visual perception in its more semantic sense of ``image interpretation'' will undeniably have a broader impact on society. From a practical point of view, image understanding systems are useful for information retrieval, surveillance, medical imaging, and in many other endeavors. In addition, the proposed activities include collaboration with industry and government agencies and involve postdocs, graduate and undergraduate students. These activities also explicitly involve younger pupils in grades 6 through 12, and will hopefully help attract them to computer vision. 0535152/0535166This project addresses the problem of category-level object recognition in images: Its aim is to develop effective methodologies for representing object classes; learning the corresponding object models from cluttered sample images in a semi-supervised manner; and efficiently and robustly recognizing instances of these models in novel images despite clutter, occlusion, viewpoint and illumination changes, and individual variations within each class. Intellectual Merit. The scientific objective of this project is to develop a representation of the salient parts of an object and their relationships that can effectively be learned fromheavily cluttered data in a weakly supervised way, correctly captures within-class variability and appearance changes due to variations in viewpoint and illumination, and effectively supports inference over object models and the automated construction of efficient classification machines.Broader Impacts. This project will investigate applications of category-level object recognition to image retrieval, video annotation, human-computer interaction; surveillance and security; and robotics via international academic and industrial collaborations. Contributions to education and outreach will include training PhD students and post-doctoral researchers, and involving underrepresented groups in graduate research and undergraduate data collection and empirical evaluation projects.

最近在计算机视觉、机器学习和组合优化方面的成功被用来再次解决计算机视觉早期定义的图像解释问题。解释是一个同时进行图像分割和区域分类的问题：给定一个图像和一个类别标签列表，目标是计算最可能的图像分割和标签，每个片段一个标签。这是在上下文中学习，因为学习技术被用来在复杂、杂乱的图像的上下文中识别多个对象。提出了一种手动图像标记方法，该方法获得了网络冲浪者和初中和高中学生的帮助来解决这个劳动密集型任务，同时让年轻学生接触计算机视觉研究。拟议的工作具有与计算机视觉，人工智能，和认知能力。特别是，该建议提出的定义“图像的单词”的概念可能会为文本检索研究建立一个新的、富有成效的桥梁，并拓宽计算机视觉与其他科学领域的讨论。从语义上理解视觉感知的"图像解释“，无疑将对社会产生更广泛的影响。从实际的角度来看，图像理解系统是有用的信息检索，监视，医学成像，并在许多其他的努力。此外，拟议的活动包括与工业和政府机构的合作，并涉及博士后，研究生和本科生。这些活动还明确涉及6至12年级的年轻学生，希望有助于吸引他们学习计算机视觉。0535152/0535166该项目解决了图像中类别级对象识别的问题：其目的是开发有效的方法来表示对象类别;以半监督的方式从杂乱的样本图像中学习相应的对象模型;并且在新的图像中有效且鲁棒地识别这些模型的实例，而不管杂乱、遮挡、视点和照明变化，以及每一类中的个体差异。智力优势。该项目的科学目标是开发一种对象的突出部分及其关系的表示，这种表示可以以弱监督的方式有效地从严重混乱的数据中学习，正确地捕获由于视点和照明变化而引起的类内变化和外观变化，并有效地支持对象模型的推理和高效分类机的自动构建。 该项目将通过国际学术和工业合作，研究类别级对象识别在图像检索、视频注释、人机交互、监控和安全以及机器人技术方面的应用。 对教育和外联的贡献将包括培训博士生和博士后研究人员，并让代表性不足的群体参与研究生研究和本科生数据收集以及经验评价项目。