RI: Large Scale Object Recognition and Ground Truth Representation Using Stochastic Image Grammar

主要研究者：Song-Chun Zhu机构：加州-洛杉矶大学题目：基于随机图像文法的大规模目标识别和地面真实值表示该项目旨在实现三个目标：（1）研究一个通用的表示框架，用于学习和建模数百个目标类别，特别是考虑到类别内的大变化;（2）构造具有最少一百万个对象的大型地面实况数据库，该数据库被半自动地注释以用于学习和测试;以及（3）构建鲁棒的大规模对象识别和图像解析系统。 这个建议的核心是一个随机的上下文敏感的图像语法有效的视觉知识表示和鲁棒的贝叶斯推理。 提出的随机图像文法结合了随机上下文无关文法（SCFG）的可重构性和图形（MRF）模型的上下文约束。这种随机语法模型具有很强的组合能力，用于表示大的类内结构变化和递归结构，用于可扩展的计算，并且可以从相对较小的样本集中学习。为了使大规模建模和学习框架实用化，PI一直在与中国的莲花山研究所（LHI）合作构建一个大规模的地面实况数据库。 目前的数据库包含50多万幅图像，这些图像是使用半自动视觉系统按等级人工分析的，分为240个物体类别和20个场景类别。所有的数据都统一表示在一个大的与或图结构的学习和测试。我们计划在项目期间继续收集和注释多达100万幅图像，并构建一系列基准。该计划开发了大规模目标识别的核心技术，可用作构建商业和国防工业广泛应用的基础，例如智能图像搜索，安全和监控，自动驾驶汽车，帮助盲人和视障人士。地面实况数据库将是世界上最大的详细解析和注释数据库。这个大型数据集的一部分将被公开用于学习和基准评估。它有望在视觉领域产生重大影响，并通过提供更真实的刺激和自然的图像统计数据来帮助研究人员研究认知科学中的人类感知。 该项目的进展情况将通过以下网页进行报告：http：civs.stat.ucla.edu/Category_Recognition

PI: Song-Chun ZhuInstitution: University of California - Los AngelesTitle: Large Scale Object Recognition and Ground Truth Representation Using Stochastic Image GrammarThe proposed project is aimed at three objectives, (1) studying a common representational framework for learning and modeling hundreds of object categories, especially to account for the large intra-category variations; (2) constructing a large ground truth database with a minimum of one million objects annotated semi-automatically for learning and testing; and (3) building a robust large scale object recognition and image parsing system. The core to this proposal is a stochastic context sensitive image grammar for effective visual knowledge representation and robust Bayesian inference. The proposed stochastic image grammar combines the reconfigurability of stochastic context free grammar (SCFG) with the contextual constraints of graphical(MRF) models. This stochastic grammar model has strong compositional power for representing large intra-class structural variations and recursive structures for scalable computing, and can be learned from a relatively small sample set. To make the large scale modeling and learning framework practical, the PI has been constructing a large scale ground truth database in collaboration with the Lotus Hill Institute (LHI) in China. The current database contains over 500,000 images manually parsed hierarchically using a semi-automatic vision system, in 240 object categories and 20 scene categories. All the data are represented uniformly in a large And-Or graph structure for learning and testing. We propose to continue collecting and annotating up to 1,000,000 images and construct a series of benchmarks during this project period.The proposal develops core techniques for large scale object recognition which can be used as the foundation for building a wide range of applications in commercial and defense industry, such as intelligence image search, security and surveillance, autonomous vehicle, and assisting the blind and visually impaired. The ground truth database shall be the world largest in its detailed parsing and annotation.A selected portion of this large dataset will be publicized for learning and benchmark evaluation.It is expected to have a significant impact in the vision community, and it may also help researchers studying human perception in cognitive science by providing more realistic stimuli and natural image statistics. Progress of this project will be reported through the following webpagehttp://civs.stat.ucla.edu/Category_Recognition