Image Parsing: Integrating Generative and Discriminative Methods

摘要：0413214。该提案将开发一种解析图像的计算框架。图像分析包括许多标准的计算机视觉任务，如目标检测和图像分割。该方法基于贝叶斯推理，采用数据驱动的马尔可夫链蒙特卡罗(DDMCMC)算法。它涉及到通过分层生成模型对自然图像中出现的各种视觉模式进行建模。它还需要一个算法DDMCMC，该算法能够对这些模型进行推理。DDMCMC的设计原理是利用判别模型通过产生式模型的参数来指导搜索。图像分析的具体应用包括开发计算机视觉系统，以通过检测和阅读文本以及检测其他显著对象(如面部)来帮助视觉残障人士。因此，我们希望这项工作对帮助视障人士产生广泛的影响。其他应用包括基于上下文的图像检索和自动安全系统。这项工作还将帮助培养2-3名计算机科学和统计方面的研究生。

Abstract: 0413214. Alan L. Yuille.The proposal will develop a computational framework for parsing images. Image parsing subsumes many standard computer vision tasks such as object detection and image segmentation. The approach is based on Bayesian inference using the Data Driven Markov Chain Monte Carlo (DDMCMC) algorithm. It involves modeling the diverse visual patterns that occur in natural images by hierarchical generative models. It also requires an algorithm, DDMCMC, that is capable of performing inference on these models. The design principle of DDMCMC is to use discriminative models to guide the search through the parameters of the generative models. Specific applications of image parsing include the development of computer vision systems to help the visually disabled by detecting and reading text, and detecting other salient objects such as faces. Hence we except this work to have broad impact in helping the visually disabled. Other applications include context based image retrieval and automatic security systems. The work will also help train 2-3 graduate students in Computer Science and Statistics.