Müller, Andreas Christian: Methods for Learning Structured Prediction in Semantic Segmentation of Natural Images. - Bonn, 2014. - Dissertation, Rheinische Friedrich-Wilhelms-Universität Bonn.
Online-Ausgabe in bonndoc: https://nbn-resolving.org/urn:nbn:de:hbz:5n-37289
@phdthesis{handle:20.500.11811/6162,
urn: https://nbn-resolving.org/urn:nbn:de:hbz:5n-37289,
author = {{Andreas Christian Müller}},
title = {Methods for Learning Structured Prediction in Semantic Segmentation of Natural Images},
school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
year = 2014,
month = sep,

note = {Automatic segmentation and recognition of semantic classes in natural images is an important open problem in computer vision. In this work, we investigate three different approaches to recognition: without supervision, with supervision on level of images, and with supervision on the level of pixels. The thesis comprises three parts.
The first part introduces a clustering algorithm that optimizes a novel information-theoretic objective function. We show that the proposed algorithm has clear advantages over standard algorithms from the literature on a wide array of datasets. Clustering algorithms are an important building block for higher-level computer vision applications, in particular for semantic segmentation.
The second part of this work proposes an algorithm for automatic segmentation and recognition of object classes in natural images, that learns a segmentation model solely from annotation in the form of presence and absence of object classes in images.
The third and main part of this work investigates one of the most popular approaches to the task of object class segmentation and semantic segmentation, based on conditional random fields and structured prediction. We investigate several learning algorithms, in particular in combination with approximate inference procedures. We show how structured models for image segmentation can be learned exactly in practical settings, even in the presence of many loops in the underlying neighborhood graphs. The introduced methods provide results advancing the state-of-the-art on two complex benchmark datasets for semantic segmentation, the MSRC-21 Dataset of RGB images and the NYU V2 Dataset or RGB-D images of indoor scenes. Finally, we introduce a software library that al- lows us to perform extensive empirical comparisons of state-of-the-art structured learning approaches. This allows us to characterize their practical properties in a range of applications, in particular for semantic segmentation and object class segmentation.},

url = {http://hdl.handle.net/20.500.11811/6162}
}

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