Unsupervised parallel image classification using Markovian models

Kató, Zoltán; Zerubia, Josiane; Berthod, Marc-Antoine

doi:10.1016/s0031-3203(98)00104-6

Cited by 72 publications

(47 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For MRF segmentation, more details can be found in [2], [4], [5], [9]. Once feature vectors are generated, the six steps of the algorithm proposed here are applied.…”

Section: Multi-layer Fusion-mrf Modelmentioning

confidence: 99%

“…II-A) on the fused images is applied following the unsupervised Kmeans clustering of the fused data. The labeling resulting from the fused segmentation is then fed into each single layers for training the Gaussian models [2], [9] of the inlayer clusters. In both experiments, we compare the performance of our new multi-layer MRF segmentation against independently processed single layer labeling [9], [10].…”

Section: Mrf Optimizationmentioning

confidence: 99%

“…For MRF segmentation unsupervised labeling is often used [9] [10]. Hierarchical models or tree structures are also applicable for Markov chain models for unsupervised texture segmentation [7] [11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Segmentation of Remote Sensing Images Using Similarity-Measure-Based Fusion-MRF Model

Szirányi

Shadaydeh

2014

IEEE Geosci. Remote Sensing Lett.

View full text Add to dashboard Cite

Abstract-Classifying segments and detecting changes in terrestrial areas are important and time-consuming efforts for remote-sensing image analysis tasks, including comparison and retrieval in repositories containing multi-temporal remote image samples for the same area in very different quality and details. We propose a multi-layer fusion model for adaptive segmentation and change detection of optical remote sensing image series, where trajectory analysis or direct comparison is not applicable. Our method applies unsupervised or partly supervised clustering on a fused image series by using cross-layer similarity measure, followed by a multi-layer MRF segmentation. The resulted labelmap is applied for the automatic training of the single layers. After the segmentation of each single layer separately, changes are detected between the single label-maps. The significant benefit of the proposed method has been numerically validated on remotely sensed image series with ground-truth data.

show abstract

“…For MRF segmentation, more details can be found in [2], [4], [5], [9]. Once feature vectors are generated, the six steps of the algorithm proposed here are applied.…”

Section: Multi-layer Fusion-mrf Modelmentioning

confidence: 99%

Section: Mrf Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Segmentation of Remote Sensing Images Using Similarity-Measure-Based Fusion-MRF Model

Szirányi

Shadaydeh

2014

IEEE Geosci. Remote Sensing Lett.

View full text Add to dashboard Cite

show abstract

“…To address the problems associated with nonhierarchical models, multiscale MRF models were formulated and have been extensively discussed in the image processing literature (Bouman and Shapiro, 1994;Kato et al 1996Kato et al , 1999Laferté et al, 2000;Liang and Tjahjadi, 2006;Mignotte et al, 2000;Wilson and Li, 2003). In those hierarchical MRF models, there is a series of random fields at a range of scales or resolutions, and the random field at each scale depends only on the next coarser random field above it.…”

Section: Related Workmentioning

confidence: 99%

“…In our multiscale MRF model, the value of a site at a given scale depends not only on its parent in the layer above but also on its neighbors at the same scale. In this respect, our model is closely related to the models presented in (Kato et al 1996(Kato et al , 1999Mignotte et al, 2000;Wilson and Li, 2003). However, unlike the models described by these authors, we solve the statistical inference problem by means of a sequence of related multi-resolution problems rather than as a single problem representing the entire quadtree.…”

Section: A Multiscale Mrf Modelmentioning

confidence: 99%

Markov Random Field Modeling of the Spatial Distribution of Proteins on Cell Membranes

Zhang¹

2007

Bull. Math. Biol.

View full text Add to dashboard Cite

Cell membranes display a range of receptors that bind ligands and activate signaling pathways. Signaling is characterized by dramatic changes in membrane molecular topography, including the co-clustering of receptors with signaling molecules and the segregation of other signaling molecules away from receptors. Electron microscopy of immunogold-labeled membranes is a critical technique to generate topographical information at the 5-10 nm resolution needed to understand how signaling complexes assemble and function. However, due to experimental limitations, only two molecular species can usually be labeled at a time. A formidable challenge is to integrate experimental data across multiple experiments where there are from 10 to 100 different proteins and lipids of interest and only the positions of two species can be observed simultaneously. As a solution, we propose the use of Markov random field (MRF) modeling to reconstruct the distribution of multiple cell membrane constituents from pair-wise data sets. MRFs are a powerful mathematical formalism for modeling correlations between states associated with neighboring sites in spatial lattices. The presence or absence of a protein of a specific type at a point on the cell membrane is a state. Since only two protein types can be observed, i.e., those bound to particles, and the rest cannot be observed, the problem is one of deducing the conditional distribution of a MRF with unobservable (hidden) states. Here, we develop a multiscale MRF model and use mathematical programming techniques to infer the conditional distribution of a MRF for proteins of three types from observations showing the spatial relationships between only two types. Application to synthesized data shows that the spatial distributions of three proteins can be reliably estimated. Application to experimental data provides the first maps of the spatial relationship between groups of three different signaling molecules. The work is an important step toward a more complete understanding of membrane spatial organization and dynamics during signaling.

show abstract

Noise Removal and Contour Detection

Charbonnier¹,

Collet²

2013

Inverse Problems in Vision and 3D Tomography

View full text Add to dashboard Cite

Unsupervised parallel image classification using Markovian models

Abstract: This paper deals with the problem of unsupervised classification of images modeled by Markov Random Fields (MRF

Cited by 72 publications

References 22 publications

Segmentation of Remote Sensing Images Using Similarity-Measure-Based Fusion-MRF Model

Segmentation of Remote Sensing Images Using Similarity-Measure-Based Fusion-MRF Model

Markov Random Field Modeling of the Spatial Distribution of Proteins on Cell Membranes

Noise Removal and Contour Detection

Contact Info

Product

Resources

About