Unsupervised Hierarchical Markovian

Connected image processing with multivariate attributes: An unsupervised Markovian classification approach

This article presents a new approach for constructing connected operators for image processing and analysis. It relies on a hierarchical Markovian unsupervised algorithm in order to classify the nodes of the traditional Max-Tree. This approach enables to naturally handle multivariate attributes in a robust non-local way. The technique is demonstrated on several image analysis tasks: filtering, ...

Unsupervised Parallel Image Classification Using a Hierarchical Markovian Model∗

This paper deals with the problem of unsupervised classification of images modeled by Markov Random Fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (simulated annealing, ICM, etc. . . ). However, when they are not known, the problem becomes more difficult. One has to estimate the hidden label field parameters from the only observabl...

High-Dimensional Unsupervised Active Learning Method

In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the da...

Evaluation of Iranian electronic products manufacturing industries using an unsupervised model, ARAS, SAW, and DEA models

Erez , and Bouthemy : Sonar Image Segmentation Using an Unsupervised Hierarchical Mrf Model 3

| This paper is concerned with hierarchical Markov Random Field (MRF) models and their application to sonar image segmentation. We present an original hierarchical seg-mentation procedure devoted to images given by a high resolution sonar. The sonar image is segmented into two kinds of regions: shadow (corresponding to a lack of acoustic reverberation behind each object lying on the sea-bed) an...

Sonar image segmentation using an unsupervised hierarchical MRF model

This paper is concerned with hierarchical Markov random field (MRP) models and their application to sonar image segmentation. We present an original hierarchical segmentation procedure devoted to images given by a high-resolution sonar. The sonar image is segmented into two kinds of regions: shadow (corresponding to a lack of acoustic reverberation behind each object lying on the sea-bed) and s...