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.

Semantic Segmentation of Remote Sensing Imagery Using Object-Based Markov Random Field Model With Regional Penalties

Abstract: This paper proposes a novel object-based Markov random field model (OMRF) for semantic segmentation of remote sensing images. First, the method employs the region size and edge information to build a weighted region adjacency graph (WRAG) for capturing the complicated interactions among objects. Thereafter, aimed at modeling object interactions in the OMRF, the size and edge information are further introduced into the Gibbs joint distribution of the random field as regional penalties. Finally, the semantic segmentation is achieved through a principled probabilistic inference of the OMRF with regional penalties. The proposed method is compared with other MRF-based methods and some state-of-the-art methods. Experiments are conducted on a series of synthetic and real-world images. Segmentation results demonstrate that our method provides better performance (an accuracy improvement about 3%). Moreover, we further discuss the application of the proposed method for classification.

An MRF Model-Based Active Learning Framework for the Spectral-Spatial Classification of Hyperspectral Imagery

Abstract: Hyperspectral image classification has attracted extensive research efforts in the recent decades. The main difficulty lies in the few labeled samples versus high dimensional features. The spectral-spatial classification method using Markov random field (MRF) has been shown to perform well in improving the classification performance. Moreover, active learning (AL), which iteratively selects the most informative unlabeled samples and enlarges the training set, has been widely studied and proven useful in remotely sensed data. In this paper, we focus on the combination of MRF and AL in the classification of hyperspectral images, and a new MRF model-based AL (MRF-AL) framework is proposed. In the proposed framework, the unlabeled samples whose predicted results vary before and after the MRF processing step is considered as uncertain. In this way, subset is firstly extracted from the entire unlabeled set, and AL process is then performed on the samples in the subset. Moreover, hybrid AL methods which combine the MRF-AL framework with either the passive random selection method or the existing AL methods are investigated. To evaluate and compare the proposed AL approaches with other state-of-the-art techniques, experiments were conducted on two hyperspectral data sets. Results demonstrated the effectiveness of the hybrid AL methods, as well as the advantage of the proposed MRF-AL framework.

Photometric stereo with coherent outlier handling and confidence estimation

Abstract: In photometric stereo a robust method is required to deal with outliers, such as shadows and non-Lambertian reflections. In this paper we rely on a probabilistic imaging model that distinguishes between inliers and outliers, and formulate the problem as a Maximum-Likelihood estimation problem. To signal which imaging model to use a hidden binary inlier map is introduced, which, to account for the fact that inlier/outlier pixels typically group together, is modelled as a Markov Random Field. To make inference of model parameters and hidden variables tractable a mean field Expectation-Maximization (EM) algorithm is used. If for each pixel we add the scaled normal, i.e. albedo and normal combined, to the model parameters, it would not be possible to obtain a confidence estimate in the result. Instead, each scaled normal is added as a hidden variable, the distribution of which, approximated by a Gaussian, is also estimated in the EM algorithm. The covariance matrix of the recovered approximate Gaussian distribution serves as a confidence estimate of the scaled normal. We demonstrate experimentally the effectiveness or our approach.

Moving Cast Shadow Removal Based on Local Descriptors

Abstract: Moving cast shadow removal is an important yet difficult problem in video analysis and applications. This paper presents a novel algorithm for detection of moving cast shadows, that based on a local texture descriptor called Scale Invariant Local Ternary Pattern (SILTP). An assumption is made that the texture properties of cast shadows bears similar patterns to those of the background beneath them. The likelihood of cast shadows is derived using information in both color and texture. An online learning scheme is employed to update the shadow model adaptively. Finally, the posterior probability of cast shadow region is formulated by further incorporating prior contextual constrains using a Markov Random Field (MRF) model. The optimal solution is found using graph cuts. Experimental results tested on various scenes demonstrate the robustness of the algorithm.