Partitionnement de données diffus

Le partitionnement diffus ou souple (en anglais, fuzzy clustering) est une forme de partitionnement de données dans laquelle chaque observation peut appartenir à plusieurs groupes (ou clusters). Le partitionnement de données implique d'assigner des observations à des groupes de telle sorte que les éléments d'un même groupe soient aussi similaires que possible, tandis que les éléments appartenant à différents groupes sont aussi dissemblables que possible. Ces groupes, ou clusters, sont identifiés par des mesures de similarité, telles qu'une distance, connectivité ou intensité. Différentes mesures de similarité peuvent être choisies en fonction des données ou de l'application. Comparaison avec le partitionnement fort Dans le partitionnement non-diffus (également appelé partitionnement fort), les données sont divisées en groupes distincts, où chaque observation ne peut appartenir qu'à un seul groupe. Dans le partitionnement diffus, les observations peuvent appartenir à plusie

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Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented.

DH-406: Machine learning for DH

This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and implement methods to analyze diverse data types, such as images, music and social network data.

MICRO-455: Applied machine learning

Real-world engineering applications must cope with a large dataset of dynamic variables, which cannot be well approximated by classical or deterministic models. This course gives an overview of methods from Machine Learning for the analysis of non-linear, highly noisy and multi dimensional data

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In this thesis, a novel method for the segmentation of video sequences based on the analysis of multiple image features is presented. A key feature of the system is the distinction between two levels of segmentation, namely region and object segmentation. Regions are homogeneous areas of the images, which are extracted automatically by the computer. Semantically meaningful objects are obtained by grouping regions, automatically or through user interaction, according to the specific application. This splitting relieves the computer of ill-posed semantic problems, and allows a higher level of flexibility in the use of the results. The extraction of the regions is based the multidimensional analysis of several image features by a spatially constrained Fuzzy C-Means algorithm. The relative weighting of the different features is achieved by means of an adaptive system that takes into account the local level of reliability of each feature. The temporal tracking of the obtained regions is performed by means of a dual strategy in which the motion-compensated projection of the segmentation mask from previous frames is used to influence the segmentation of the current frame so as to achieve higher temporal coherence and stability.

EPFL1998

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Automatic extraction of geometric lip features with application to multi-modal speaker identification

Ivana Arsic de Heras Ciechomska, Jean-Philippe Thiran

In this paper we consider the problem of automatic extraction of the geometric lip features for the purposes of multi-modal speaker identification. The use of visual information from the mouth region can be of great importance for improving the speaker identification system performance in noisy conditions. We propose a novel method for automated lip features extraction that utilizes color space transformation and a fuzzy-based c-means clustering technique. Using the obtained visual cues closed-set audio-visual speaker identification experiments are performed on the CUAVE database, [1] showing promising results.

IEEE2006

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Image Clustering Algorithms to Identify Complicated Cerebral Diseases. Description and Comparison

Maria-Alexandra Paun

This article presents two algorithms developed based on two different techniques, from clusterization theory, namely k-means clustering technique and Fuzzy C-means technique, respectively. In this context, the study offers a sustained comparison of the two algorithms in order to properly choose one of them, depending on the image to be analyzed and the solution that is desired. Algorithms are used in image processing, respectively as application of image processing techniques in brain computed tomography image analysis. There were also compared the results obtained by running the algorithms with a different number of centroids, as well as the execution times of each algorithm in part. Image processing and obtaining the results presented in this document was made possible by using the MATLAB R2018b environment. This fact is possible because some components of the brain, such as the blood vessel network or the neural network, have a fractal arrangement, which makes it easy to analyze their structure, in order to provide predictions or treatments to patients in discussion afflicted with a serious brain disease, as accurately as possible.

2020

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