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Global-Context Refinement for Semantic Image Segmentation

Menart, Christopher J, Menart
http://rave.ohiolink.edu/etdc/view?acc_num=osu1523462175806808

Abstract Details

2018, Master of Science, Ohio State University, Computer Science and Engineering.
Convolutional neural nets have been applied to the task of semantic image segmentation and surpassed previous methods. But even state-of-the-art systems fail on many portions of modern segmentation datasets. We observe that these failures are not random, but in most cases systematic and partially predictable. In particular, the confusion of a segmentation model is mostly stable. We propose compact descriptors of classifier behavior and of visual scene type. These descriptors can be applied in a Bayesian framework to reason about the reliability of predictions returned by a semantic segmentation model, and to correct mistakes in those results contingent on the ability to characterize images at the scene level. We demonstrate, using a competitive semantic segmentation model and several challenging datasets, that the upper bound of this approach is a great improvement in accuracy. The future work we describe has the potential to yield flexible and broad-ranging improvements to deep scene understanding and similar classification problems.
Jim Davis, Ph.D. (Advisor)
Eric Fosler-Lussier, Ph.D. (Committee Member)
52 p.
Artificial Intelligence; Computer Science
computer vision; artificial intelligence; deep learning; convolutional neural nets; semantic segmentation; image context; probabilistic inference; bayesian; PASCAL Context; NYUDv2; ADE20K; RefineNet; deep scene understanding; confusion matrices

Recommended Citations

Citations

  • Menart, Menart, C. J. (2018). Global-Context Refinement for Semantic Image Segmentation [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523462175806808

    APA Style (7th edition)

  • Menart, Menart, Christopher. Global-Context Refinement for Semantic Image Segmentation. 2018. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1523462175806808.

    MLA Style (8th edition)

  • Menart, Menart, Christopher. "Global-Context Refinement for Semantic Image Segmentation." Master's thesis, Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523462175806808

    Chicago Manual of Style (17th edition)

osu1523462175806808
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This open access ETD is published by The Ohio State University and OhioLINK.