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Scalable Optimization Methods with Side Information in Image Understanding

机译：图像理解中带有辅助信息的可扩展优化方法

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摘要

The field of Computer Vision includes a highly varied collection of technologies for using Artificial Intelligence to understand and process images. A common thread throughout Computer Vision is mathematical optimization, frequently used as a tool to model and solve these problems. A key advantage of optimization formulations is that a model of any basic Computer Vision problem can be extended to include additional information and requirements. Prior knowledge, side information, and application-specific restrictions can be expressed within the objective or constraints. This provides a general way to formally pose modifications to common vision algorithms. It is not entirely sufficient, however, to only describe an optimization model that includes the desired side information. The wide array of problems that can be formulated this way is accompanied by a similarly wide range of computational difficulty. While the optimization problem for a standard Computer Vision problem may be solved by a simple and efficient algorithm, with included side information the extended problem can be fundamentally harder and require more complex solvers.;The focus of this dissertation is a set of vision applications in image segmentation, clustering, and classification that include side information. For the extended problems, I describe scalable and distributed algorithms that allow even the harder optimizations to be solved efficiently. In the case of segmentation, the inference is extended to consider multiple images related by the presence of a common foreground, with an interactive implementation that can parallelize across the computational units of a Graphics Processing Unit (GPU). Then, a distributed image clustering algorithm that can incorporate side constraints is presented. The final problem that is considered is the use of side constraints in neural network training to build image classifiers with reduced memory requirements. This dissertation shows that modeling Computer Vision problems as an optimization effectively provides a way both to reason about the kinds application-specific extensions presented in these examples and to make finding a solution fast and efficient.
机译：计算机视觉领域包括用于使用人工智能理解和处理图像的多种技术。整个Computer Vision的共同点是数学优化，通常用作建模和解决这些问题的工具。优化公式的主要优势在于，可以扩展任何基本计算机视觉问题的模型，以包括其他信息和要求。可以在目标或约束内表达先验知识，辅助信息和特定于应用程序的限制。这提供了一种对普通视觉算法进行形式化修改的通用方法。但是，仅描述包括所需辅助信息的优化模型并不完全足够。可以用这种方式解决的各种各样问题都伴随着类似范围的计算困难。虽然可以通过简单高效的算法来解决标准计算机视觉问题的优化问题，但随着附带信息的出现，扩展问题从根本上讲可能更加困难，并且需要更复杂的求解器。本论文的重点是一组视觉应用包含辅助信息的图像分割，聚类和分类。对于扩展问题，我描述了可扩展的分布式算法，这些算法甚至可以更有效地解决更难的优化问题。在分割的情况下，推论被扩展为考虑通过共同的前景的存在而相关的多个图像，其交互实现可以在图形处理单元（GPU）的计算单元之间并行化。然后，提出了一种可以结合侧面约束的分布式图像聚类算法。需要考虑的最后一个问题是在神经网络训练中使用侧约束来构建具有减少的内存需求的图像分类器。本文表明，将计算机视觉问题建模为一种优化有效地提供了一种方法，既可以推理这些示例中提出的各种特定于应用程序的扩展，又可以快速有效地找到解决方案。

著录项

  • 作者

    Collins, Maxwell D.;

  • 作者单位

    The University of Wisconsin - Madison.;

  • 授予单位 The University of Wisconsin - Madison.;
  • 学科 Computer science.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 159 p.
  • 总页数 159
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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