scispace - formally typeset
Search or ask a question
Topic

Markov random field

About: Markov random field is a research topic. Over the lifetime, 5669 publications have been published within this topic receiving 179568 citations. The topic is also known as: MRF.


Papers
More filters
Proceedings ArticleDOI
20 Jun 2011
TL;DR: This paper proposes to recognize collective activities using the crowd context and introduces a new scheme for learning it automatically, constructed upon a Random Forest structure which randomly samples variable volume spatio-temporal regions to pick the most discriminating attributes for classification.
Abstract: In this paper we present a framework for the recognition of collective human activities. A collective activity is defined or reinforced by the existence of coherent behavior of individuals in time and space. We call such coherent behavior ‘Crowd Context’. Examples of collective activities are “queuing in a line” or “talking”. Following [7], we propose to recognize collective activities using the crowd context and introduce a new scheme for learning it automatically. Our scheme is constructed upon a Random Forest structure which randomly samples variable volume spatio-temporal regions to pick the most discriminating attributes for classification. Unlike previous approaches, our algorithm automatically finds the optimal configuration of spatio-temporal bins, over which to sample the evidence, by randomization. This enables a methodology for modeling crowd context. We employ a 3D Markov Random Field to regularize the classification and localize collective activities in the scene. We demonstrate the flexibility and scalability of the proposed framework in a number of experiments and show that our method outperforms state-of-the art action classification techniques [7, 19].

266 citations

Journal ArticleDOI
TL;DR: It is argued that it can restate both tasks as that of fitting a GMRF to a prescribed stationary Gaussian field on a lattice when both local and global properties are important, and that GMRFs with small neighbourhoods can approximate Gaussian fields surprisingly well even with long correlation lengths.
Abstract: This paper discusses the following task often encountered in building Bayesian spatial models: construct a homogeneous Gaussian Markov random field (GMRF) on a lattice with correlation properties either as present in some observed data, or consistent with prior knowledge. The Markov property is essential in designing computationally efficient Markov chain Monte Carlo algorithms to analyse such models. We argue that we can restate both tasks as that of fitting a GMRF to a prescribed stationary Gaussian field on a lattice when both local and global properties are important. We demonstrate that using the Kullback-Leibler discrepancy often fails for this task, giving severely undesirable behaviour of the correlation function for lags outside the neighbourhood. We propose a new criterion that resolves this difficulty, and demonstrate that GMRFs with small neighbourhoods can approximate Gaussian fields surprisingly well even with long correlation lengths. Finally, we discuss implications of our findings for likelihood based inference for general Markov random fields when global properties are also important.

257 citations

Proceedings ArticleDOI
01 Sep 2009
TL;DR: The model proposed here bypasses measurement of the histogram differences in a direct fashion and enables obtaining efficient solutions to the underlying optimization model, and can be solved to optimality in polynomial time using a maximum flow procedure on an appropriately constructed graph.
Abstract: This paper is focused on the Co-segmentation problem [1] - where the objective is to segment a similar object from a pair of images. The background in the two images may be arbitrary; therefore, simultaneous segmentation of both images must be performed with a requirement that the appearance of the two sets of foreground pixels in the respective images are consistent. Existing approaches [1, 2] cast this problem as a Markov Random Field (MRF) based segmentation of the image pair with a regularized difference of the two histograms - assuming a Gaussian prior on the foreground appearance [1] or by calculating the sum of squared differences [2]. Both are interesting formulations but lead to difficult optimization problems, due to the presence of the second (histogram difference) term. The model proposed here bypasses measurement of the histogram differences in a direct fashion; we show that this enables obtaining efficient solutions to the underlying optimization model. Our new algorithm is similar to the existing methods in spirit, but differs substantially in that it can be solved to optimality in polynomial time using a maximum flow procedure on an appropriately constructed graph. We discuss our ideas and present promising experimental results.

257 citations

Posted Content
TL;DR: A deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN) is proposed and the candidate set of motion kernels predicted by the CNN are extended using carefully designed image rotations.
Abstract: In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN). We further extend the candidate set of motion kernels predicted by the CNN using carefully designed image rotations. A Markov random field model is then used to infer a dense non-uniform motion blur field enforcing motion smoothness. Finally, motion blur is removed by a non-uniform deblurring model using patch-level image prior. Experimental evaluations show that our approach can effectively estimate and remove complex non-uniform motion blur that is not handled well by previous approaches.

255 citations

Journal ArticleDOI
TL;DR: A new and fast algorithm which computes an exact solution in the discrete framework of the discrete original problem is proposed and it is shown that minimization of total variation under L1 data fidelity term yields a self-dual contrast invariant filter.
Abstract: This paper deals with the total variation minimization problem in image restoration for convex data fidelity functionals. We propose a new and fast algorithm which computes an exact solution in the discrete framework. Our method relies on the decomposition of an image into its level sets. It maps the original problems into independent binary Markov Random Field optimization problems at each level. Exact solutions of these binary problems are found thanks to minimum cost cut techniques in graphs. These binary solutions are proved to be monotone increasing with levels and yield thus an exact solution of the discrete original problem. Furthermore we show that minimization of total variation under L 1 data fidelity term yields a self-dual contrast invariant filter. Finally we present some results.

254 citations


Network Information
Related Topics (5)
Image segmentation
79.6K papers, 1.8M citations
94% related
Convolutional neural network
74.7K papers, 2M citations
93% related
Feature extraction
111.8K papers, 2.1M citations
92% related
Image processing
229.9K papers, 3.5M citations
91% related
Deep learning
79.8K papers, 2.1M citations
91% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
202330
2022128
202196
2020173
2019204