@conference {Marquez-Neila2014,
	title = {Non-parametric higher-order random fields for image segmentation},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8694 LNCS},
	number = {PART 6},
	year = {2014},
	pages = {269{\textendash}284},
	abstract = {Models defined using higher-order potentials are becoming increasingly popular in computer vision. However, the exact representation of a general higher-order potential defined over many variables is computationally unfeasible. This has led prior works to adopt parametric potentials that can be compactly represented. This paper proposes a non-parametric higher-order model for image labeling problems that uses a patch-based representation of its potentials. We use the transformation scheme of [11, 25] to convert the higher-order potentials to a pair-wise form that can be handled using traditional inference algorithms. This representation is able to capture structure, geometrical and topological information of labels from training data and to provide more precise segmentations. Other tasks such as image denoising and reconstruction are also possible. We evaluate our method on denoising and segmentation problems with synthetic and real images. {\textcopyright} 2014 Springer International Publishing.},
	keywords = {biomedical image analysis, higher-order models, image denoising, Image segmentation, random fields, structured prediction},
	isbn = {9783319105987},
	issn = {16113349},
	doi = {10.1007/978-3-319-10599-4_18},
	author = {M{\'a}rquez-Neila, Pablo and Kohli, Pushmeet and Carsten Rother and Baumela, Luis}
}
@article {Kohli2012,
	title = {User-centric learning and evaluation of interactive segmentation systems},
	journal = {International Journal of Computer Vision},
	volume = {100},
	number = {3},
	year = {2012},
	month = {dec},
	pages = {261{\textendash}274},
	abstract = {Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. In this paper, we study the problem of evaluating and learning interactive segmentation systems which are extensively used in the real world. The key questions in this context are how to measure (1) the effort associated with a user interaction, and (2) the quality of the segmentation result as perceived by the user. We conduct a user study to analyze user behavior and answer these questions. Using the insights obtained from these experiments, we propose a framework to evaluate and learn interactive segmentation systems which brings the user in the loop. The framework is based on the use of an active robot user-a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem. {\textcopyright} 2012 The Author(s).},
	keywords = {Image segmentation, Interactive systems, Learning},
	issn = {09205691},
	doi = {10.1007/s11263-012-0537-4},
	author = {Kohli, Pushmeet and Nickisch, Hannes and Carsten Rother and Rhemann, Christoph}
}
