Quadratic Pseudo-boolean Optimization Research Articles

Phase unwrapping method based on improved quadratic pseudo-Boolean optimization.

Maximum a posteriori estimation of Markov random fields (MRFs) is a popular research area in computer vision, and many algorithms have been proposed to deal with these types of problems. The phase-unwrapping problem is modeled as the optimization of MRFs in this research, and the binary algorithm, improved quadratic pseudo-Boolean optimization, is utilized to solve the phase-unwrapping problem. Both the interferometric phases generated from the commonly used computer simulated surfaces and also real terrains are researched in the experimental section, and the unwrapping results are compared. The proposed algorithm achieves unwrapping results comparable to the state-of-the-art unwrapping method but costs less time for large-scale phase images.

Interacting Tracklets for Multi-object Tracking.

In this paper, we propose to exploit the interactions between non-associable tracklets to facilitate multi-object tracking. We introduce two types of tracklet interactions, close interaction and distant interaction. The close interaction imposes physical constraints between two temporally overlapping tracklets and more importantly, allows us to learn local classifiers to distinguish targets that are close to each other in the spatiotemporal domain. The distant interaction, on the other hand, accounts for the higher-order motion and appearance consistency between two temporally isolated tracklets. Our approach is modeled as a binary labeling problem and solved using the efficient Quadratic Pseudo-Boolean Optimization (QPBO). It yields promising tracking performance on the challenging PETS09 and MOT16 dataset. Our code will be made publicly available upon the acceptance of the manuscript.

Higher Order Energies for Image Segmentation.

A novel energy minimization method for general higher order binary energy functions is proposed in this paper. We first relax a discrete higher order function to a continuous one, and use the Taylor expansion to obtain an approximate lower order function, which is optimized by the quadratic pseudo-Boolean optimization or other discrete optimizers. The minimum solution of this lower order function is then used as a new local point, where we expand the original higher order energy function again. Our algorithm does not restrict to any specific form of the higher order binary function or bring in extra auxiliary variables. For concreteness, we show an application of segmentation with the appearance entropy, which is efficiently solved by our method. Experimental results demonstrate that our method outperforms the state-of-the-art methods.

Multi-scale graph-cut algorithm for efficient water-fat separation.

To improve the accuracy and robustness to noise in water-fat separation by unifying the multiscale and graph cut based approaches to B0 -correction. A previously proposed water-fat separation algorithm that corrects for B0 field inhomogeneity in 3D by a single quadratic pseudo-Boolean optimization (QPBO) graph cut was incorporated into a multi-scale framework, where field map solutions are propagated from coarse to fine scales for voxels that are not resolved by the graph cut. The accuracy of the single-scale and multi-scale QPBO algorithms was evaluated against benchmark reference datasets. The robustness to noise was evaluated by adding noise to the input data prior to water-fat separation. Both algorithms achieved the highest accuracy when compared with seven previously published methods, while computation times were acceptable for implementation in clinical routine. The multi-scale algorithm was more robust to noise than the single-scale algorithm, while causing only a small increase (+10%) of the reconstruction time. The proposed 3D multi-scale QPBO algorithm offers accurate water-fat separation, robustness to noise, and fast reconstruction. The software implementation is freely available to the research community. Magn Reson Med 78:941-949, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

FUSION OF HYPERSPECTRAL AND VHR MULTISPECTRAL IMAGE CLASSIFICATIONS IN URBAN α–AREAS

Abstract. An energetical approach is proposed for classification decision fusion in urban areas using multispectral and hyperspectral imagery at distinct spatial resolutions. Hyperspectral data provides a great ability to discriminate land-cover classes while multispectral data, usually at higher spatial resolution, makes possible a more accurate spatial delineation of the classes. Hence, the aim here is to achieve the most accurate classification maps by taking advantage of both data sources at the decision level: spectral properties of the hyperspectral data and the geometrical resolution of multispectral images. More specifically, the proposed method takes into account probability class membership maps in order to improve the classification fusion process. Such probability maps are available using standard classification techniques such as Random Forests or Support Vector Machines. Classification probability maps are integrated into an energy framework where minimization of a given energy leads to better classification maps. The energy is minimized using a graph-cut method called quadratic pseudo-boolean optimization (QPBO) with α-expansion. A first model is proposed that gives satisfactory results in terms of classification results and visual interpretation. This model is compared to a standard Potts models adapted to the considered problem. Finally, the model is enhanced by integrating the spatial contrast observed in the data source of higher spatial resolution (i.e., the multispectral image). Obtained results using the proposed energetical decision fusion process are shown on two urban multispectral/hyperspectral datasets. 2-3% improvement is noticed with respect to a Potts formulation and 3-8% compared to a single hyperspectral-based classification.

NestDE: generic parameters tuning for automatic story segmentation

Parameters tuning is a crucial task in automatic story segmentation. For most previous story segmentation methods, however, the parameters were simply derived from empirical tuning, which may indeed harm the fairness of the evaluation, or even misguide the conclusion. In this paper, we present a general parameters tuning approach, namely nested differential evolution. As a practical general-purpose parameters tuner, our approach itself is parameters-robust and is generic enough to optimize the most usual types of parameters for the given corpus and evaluation criterion. Besides, our approach is able to cooperate with empirical tuning and jointly produce better parameters based on the prior knowledge of experienced users. Extensive experiments on synthetic challenging quadratic pseudo-Boolean optimization and real-world story segmentation tasks validate the superior performance of our approach over traditional empirical tuning and other generic optimizers, such as simulated annealing and classical differential evolution.

Video Object Co-Segmentation via Subspace Clustering and Quadratic Pseudo-Boolean Optimization in an MRF Framework

Multiple videos may share a common foreground object, for instance a family member in home videos, or a leading role in various clips of a movie or TV series. In this paper, we present a novel method for co-segmenting the common foreground object from a group of video sequences. The issue was seldom touched on in the literature. Starting from over-segmentation of each video into Temporal Superpixels (TSPs), we first propose a new subspace clustering algorithm which segments the videos into consistent spatio-temporal regions with multiple classes, such that the common foreground has consistent labels across different videos. The subspace clustering algorithm exploits the fact that across different videos the common foreground shares similar appearance features, while motions can be used to better differentiate regions within each video, making accurate extraction of object boundaries easier. We further formulate video object co-segmentation as a Markov Random Field (MRF) model which imposes the constraint of foreground model automatically computed or specified with little user effort. The Quadratic Pseudo-Boolean Optimization (QPBO) is used to generate the results. Experiments show that this video co-segmentation framework can achieve good quality foreground extraction results without user interaction for those videos with unrelated background, and with only moderate user interaction for those videos with similar background. Comparisons with previous work also show the superiority of our approach.

Markov random field based phase demodulation of interferometric images

We present a novel method to solve the sign ambiguity for phase demodulation from a single interferometric image that possibly contains closed fringes. The problem is formulated in a Markov random field (MRF) energy minimization framework with the assumption of phase gradient orientation continuity. The binary pairwise objective function is non-submodular and therefore its minimization is an NP-hard problem, for which we devise a multigrid hierarchy of quadratic pseudoboolean optimization problems that can be improved iteratively to approximate the optimal solution. We name the method MSARI algorithm, for Markov based sign ambiguity resolution in interferometry. Compared with traditional path-following phase demodulation methods, the new approach does not require any heuristic scanning strategy, is not subject to the propagation of error, and the extension to three dimensional fringe patterns is straightforward. A set of experiments with synthetic data and real prelens tear film interferometric images of the human eye demonstrate the effectiveness and robustness of the proposed algorithm as compared with existing state-of-the-art phase demodulation methods.

Optimal Feature Matching for 3D Reconstruction by Combination of Global and Local Information

Abstract For feature matching in 3D computer vision, there are two main kinds of methods, i.e. global-based and local-based algorithms. Although both have achieved some useful results, they still have own disadvantages. This paper proposes a novel method which combines the global and local information as much as possible so that it can take both advantages. A series of sub-pixel window correlation method is employed with the guidance of fronto-parallel result to produce some local results. These local results are then repeatedly merged by quadratic pseudo-boolean optimization under the guidance of global information. After several sub-pixel local optimizations, the error rates at high resolution are tremendously reduced. When combining the global and local traits together, the third step optimization can both reduce the low resolution error as well as keep high-accuracy resolution error low. Compared with other existing algorithms, the proposed approach performs well when the scene is comprised with plana...

A fast bioluminescent source localization method based on generalized graph cuts with mouse model validations

Bioluminescence imaging (BLI) makes it possible to elucidate molecular and cellular signatures to better understand the effects of human disease in small animal models in vivo. The unambiguous three-dimensional bioluminescent source information obtained by bioluminescence tomography (BLT) could further facilitate its applications in biomedicine. However, to the best of our knowledge, the existing gradient-type reconstruction methods in BLT are inefficient, and often require a relatively small volume of interest (VOI) for feasible results. In this paper, a fast generalized graph cuts based reconstruction method for BLT is presented, which is to localize the bioluminescent source in heterogeneous mouse tissues via max-flow/min-cut algorithm. Since the original graph cuts theory can only handle graph-representable problem, the quadratic pseudo-boolean optimization is incorporated to make the graph representable and tractable, which is called generalized graph cuts (GGC). The internal light source can be reconstructed from the whole domain, so a priori knowledge of VOI can be avoided in this method. In the simulation validations, the proposed method was validated in a heterogeneous mouse atlas, and the source can be localized reliably and efficiently by GGC; and compared with gradient-type method, the proposed method is about 25-50 times faster. Moreover, the experiments for sensitivity to the measurement errors of tissue optical properties demonstrate that, the reconstruction quality is not much affected by mismatch of parameters. In what follows, in vivo mouse BLT reconstructions further demonstrated the potential and effectiveness of the generalized graph cut based reconstruction method.

Pseudo-Boolean optimization

This survey examines the state of the art of a variety of problems related to pseudo-Boolean optimization, i.e. to the optimization of set functions represented by closed algebraic expressions. The main parts of the survey examine general pseudo-Boolean optimization, the specially important case of quadratic pseudo-Boolean optimization (to which every pseudo-Boolean optimization can be reduced), several other important special classes, and approximation algorithms.