NAUTILUS method: An interactive technique in multiobjective optimization based on the nadir point

Miettinen, Kaisa; Eskelinen, Petri; Ruiz, Francisco; Luque, Mariano

doi:10.1016/j.ejor.2010.02.041

Cited by 55 publications

(56 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…An example use case is examining Dice coefficients for different structures simultaneously. There are approaches in the literature, that deal with the trade-off of multiple objective functions [42], [29]. We believe their work can help in improving Tuner.…”

Section: Lessons Learned and Future Workmentioning

confidence: 99%

Tuner: Principled Parameter Finding for Image Segmentation Algorithms Using Visual Response Surface Exploration

Torsney-Weir

Saad

Möller

et al. 2011

IEEE Trans. Visual. Comput. Graphics

105

View full text Add to dashboard Cite

Abstract-In this paper we address the difficult problem of parameter-finding in image segmentation. We replace a tedious manual process that is often based on guess-work and luck by a principled approach that systematically explores the parameter space. Our core idea is the following two-stage technique: We start with a sparse sampling of the parameter space and apply a statistical model to estimate the response of the segmentation algorithm. The statistical model incorporates a model of uncertainty of the estimation which we use in conjunction with the actual estimate in (visually) guiding the user towards areas that need refinement by placing additional sample points. In the second stage the user navigates through the parameter space in order to determine areas where the response value (goodness of segmentation) is high. In our exploration we rely on existing ground-truth images in order to evaluate the "goodness" of an image segmentation technique. We evaluate its usefulness by demonstrating this technique on two image segmentation algorithms: a three parameter model to detect microtubules in electron tomograms and an eight parameter model to identify functional regions in dynamic Positron Emission Tomography scans.Index Terms-Parameter exploration, Image segmentation, Gaussian Process Model. MOTIVATIONFor visual analysis image data often need to be segmented. Segmentation refers to the process of partitioning the image into multiple segments, i.e. sets of pixels or voxels, that form contiguous and semantically meaningful regions. If each of these regions is marked by a unique identifier, image segmentation simply means labelling of pixels or voxels. In biomedical imaging, where images are acquired using some kind of tomography or microscopy, segmented regions might correspond to anatomical structures in the case of nonfunctional imaging, and to regions with specific physiological activity in the case of functional imaging.In recent years a variety of semi-and fully automatic techniques have been developed to address the segmentation problem [32]. However, even the current state-of-the-art approaches fall short of providing a "silver bullet" for image segmentation. This has several reasons. One reason is that given some image, the segmentation problem is not well defined; in fact it depends on the application which regions are semantically meaningful. Another reason is that due to different image degradation factors such as low signal-to-noise ratio, imaging artifacts, partial volume effects and shape variability, different kinds of a priori knowledge need to be included. Additionally, the majority of the existing segmentation methods rely on and are sensitive to setting a number of parameters. For example, most of the algorithms contain weighting parameters between multiple competing image-driven or prior-driven cost terms in an attempt to mimic the cognitive capabilities of expert users (e.g. radiologists for medical images).A good parameter setting is usually found by a manual trial and error procedure. Th...

show abstract

Section: Lessons Learned and Future Workmentioning

confidence: 99%

Tuner: Principled Parameter Finding for Image Segmentation Algorithms Using Visual Response Surface Exploration

Torsney-Weir

Saad

Möller

et al. 2011

IEEE Trans. Visual. Comput. Graphics

105

View full text Add to dashboard Cite

show abstract

“…Therefore, δ h expresses improvement proportions for each objective function, from the previous iteration z h−1 . Several ways to build this direction if improvement are described in Miettinen et al (2010) and Miettinen et al (2015). Let us briefly classify and summarize them as follows.…”

Section: Preference Elicitation Modulementioning

confidence: 99%

“…Among others, the NAUTILUS methods that have been published before can be derived from the framework. The original NAUTILUS method was presented in Miettinen et al (2010) where the decision maker provides preference information by ranking the objective functions according to the relative importance of improving their current values or distributing points indicating how much improvement is needed in the current values. However, this was regarded as a very rough approach and in Miettinen et al (2015) an another technique was proposed where the decision maker provides ratios of improvement and a direction of simultaneous improvement is determined from them.…”

Section: Introductionmentioning

confidence: 99%

NAUTILUS framework: towards trade-off-free interaction in multiobjective optimization

Miettinen

Ruiz

2016

J Bus Econ

Self Cite

View full text Add to dashboard Cite

In this paper, we present a framework of different interactive NAU-TILUS methods for multiobjective optimization. In interactive methods, the decision maker iteratively sees solution alternatives and provides one's preferences in order to find the most preferred solution. We question the widely used setting that the solutions shown to the decision maker should all be Pareto optimal which implies that improvement in any objective function necessitates allowing impairment in some others. Instead, in NAUTILUS we enable the decision maker to make a free search without having to trade-off by starting from an inferior solution and iteratively approaching the Pareto optimal set by allowing all objective functions to improve. The framework presented consists of different modules for preference elicitation and optimization. Four main NAUTILUS method variants are introduced as well as ideas of utilizing the framework in a flexible way to derive further variants.

show abstract

“…The IND- NIMBUS software framework contains tools implementing and testing interactive optimization methods, which can be utilized also by the GAMS-NIMBUS Tool. Currently, there are several new interactive methods being implementated for the IND-NIMBUS framework, such as Pareto Navigator [9], PAINT [17] and Nautilus [30], and in the future, the GAMS-NIMBUS Tool can be used to apply these methods along with the NIMBUS method.…”

Section: Example 1: Power Generation Problemmentioning

confidence: 99%

Implementation aspects of interactive multiobjective optimization for modeling environments: the case of GAMS-NIMBUS

2014

Self Cite

View full text Add to dashboard Cite

NAUTILUS method: An interactive technique in multiobjective optimization based on the nadir point

Cited by 55 publications

References 12 publications

Tuner: Principled Parameter Finding for Image Segmentation Algorithms Using Visual Response Surface Exploration

Tuner: Principled Parameter Finding for Image Segmentation Algorithms Using Visual Response Surface Exploration

NAUTILUS framework: towards trade-off-free interaction in multiobjective optimization

Implementation aspects of interactive multiobjective optimization for modeling environments: the case of GAMS-NIMBUS

Contact Info

Product

Resources

About