scholarly journals Automatic Representation and Segmentation of Video Sequences via a Novel Framework Based on the nD-EVM and Kohonen Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-34
Author(s):  
José-Yovany Luis-García ◽  
Ricardo Pérez-Aguila
Keyword(s):  
Video Segmentation ◽  
Dimensional Space ◽  
Event Analysis ◽  
Self Organizing Maps ◽  
Video Representation ◽  
Guided Surgery ◽  
Frame Sequence ◽  
Video Application ◽  
Discrete Compactness ◽  
Kohonen Networks

Recently in the Computer Vision field, a subject of interest, at least in almost every video application based on scene content, is video segmentation. Some of these applications are indexing, surveillance, medical imaging, event analysis, and computer-guided surgery, for naming some of them. To achieve their goals, these applications need meaningful information about a video sequence, in order to understand the events in its corresponding scene. Therefore, we need semantic information which can be obtained from objects of interest that are present in the scene. In order to recognize objects we need to compute features which aid the finding of similarities and dissimilarities, among other characteristics. For this reason, one of the most important tasks for video and image processing is segmentation. The segmentation process consists in separating data into groups that share similar features. Based on this, in this work we propose a novel framework for video representation and segmentation. The main workflow of this framework is given by the processing of an input frame sequence in order to obtain, as output, a segmented version. For video representation we use the Extreme Vertices Model in the n-Dimensional Space while we use the Discrete Compactness descriptor as feature and Kohonen Self-Organizing Maps for segmentation purposes.

A New Method for Emulating Self-Organizing Maps for Visualization of Datasets

2018 ◽  
Vol 17 (03) ◽  
pp. 1850014 ◽  
Author(s):  
Macario O. Cordel ◽  
Arnulfo P. Azcarraga
Keyword(s):  
Disaster Response ◽  
Dimensional Space ◽  
Main Idea ◽  
Cost Effective ◽  
Fine Tuning ◽  
Visualization Tool ◽  
Self Organizing Map ◽  
Text Complexity ◽  
Self Organizing Maps ◽  
Self Organizing

Several time-critical problems relying on large amount of data, e.g., business trends, disaster response and disease outbreak, require cost-effective, timely and accurate data summary and visualization, in order to come up with an efficient and effective decision. Self-organizing map (SOM) is a very effective data clustering and visualization tool as it provides intuitive display of data in lower-dimensional space. However, with [Formula: see text] complexity, SOM becomes inappropriate for large datasets. In this paper, we propose a force-directed visualization method that emulates SOMs capability to display the data clusters with [Formula: see text] complexity. The main idea is to perform a force-directed fine-tuning of the 2D representation of data. To demonstrate the efficiency and the vast potential of the proposed method as a fast visualization tool, the methodology is used to do a 2D-projection of the MNIST handwritten digits dataset.

scholarly journals Current Projection Methods-Induced Biases at Subgroup Detection for Machine-Learning Based Data-Analysis of Biomedical Data

2019 ◽  
Vol 21 (1) ◽  
pp. 79 ◽  
Author(s):  
Jörn Lötsch ◽  
Alfred Ultsch
Keyword(s):  
Dimensional Space ◽  
Cluster Structure ◽  
Projection Methods ◽  
High Dimensional ◽  
Computational Techniques ◽  
Correct Identification ◽  
Data Sets ◽  
Biomedical Data ◽  
Self Organizing Maps ◽  
Wrong Number

Advances in flow cytometry enable the acquisition of large and high-dimensional data sets per patient. Novel computational techniques allow the visualization of structures in these data and, finally, the identification of relevant subgroups. Correct data visualizations and projections from the high-dimensional space to the visualization plane require the correct representation of the structures in the data. This work shows that frequently used techniques are unreliable in this respect. One of the most important methods for data projection in this area is the t-distributed stochastic neighbor embedding (t-SNE). We analyzed its performance on artificial and real biomedical data sets. t-SNE introduced a cluster structure for homogeneously distributed data that did not contain any subgroup structure. In other data sets, t-SNE occasionally suggested the wrong number of subgroups or projected data points belonging to different subgroups, as if belonging to the same subgroup. As an alternative approach, emergent self-organizing maps (ESOM) were used in combination with U-matrix methods. This approach allowed the correct identification of homogeneous data while in sets containing distance or density-based subgroups structures; the number of subgroups and data point assignments were correctly displayed. The results highlight possible pitfalls in the use of a currently widely applied algorithmic technique for the detection of subgroups in high dimensional cytometric data and suggest a robust alternative.

scholarly journals Data-driven clustering of rain events: microphysics information derived from macro-scale observations

2017 ◽  
Vol 10 (4) ◽  
pp. 1557-1574 ◽  
Author(s):  
Mohamed Djallel Dilmi ◽  
Cécile Mallet ◽  
Laurent Barthes ◽  
Aymeric Chazottes
Keyword(s):  
Rain Rate ◽  
Dimensional Space ◽  
Hierarchical Cluster ◽  
Rate Variation ◽  
Rain Event ◽  
Self Organizing Maps ◽  
Macro Scale ◽  
Minimal Subset ◽  
Rain Events

Abstract. Rain time series records are generally studied using rainfall rate or accumulation parameters, which are estimated for a fixed duration (typically 1 min, 1 h or 1 day). In this study we use the concept of rain events. The aim of the first part of this paper is to establish a parsimonious characterization of rain events, using a minimal set of variables selected among those normally used for the characterization of these events. A methodology is proposed, based on the combined use of a genetic algorithm (GA) and self-organizing maps (SOMs). It can be advantageous to use an SOM, since it allows a high-dimensional data space to be mapped onto a two-dimensional space while preserving, in an unsupervised manner, most of the information contained in the initial space topology. The 2-D maps obtained in this way allow the relationships between variables to be determined and redundant variables to be removed, thus leading to a minimal subset of variables. We verify that such 2-D maps make it possible to determine the characteristics of all events, on the basis of only five features (the event duration, the peak rain rate, the rain event depth, the standard deviation of the rain rate event and the absolute rain rate variation of the order of 0.5). From this minimal subset of variables, hierarchical cluster analyses were carried out. We show that clustering into two classes allows the conventional convective and stratiform classes to be determined, whereas classification into five classes allows this convective–stratiform classification to be further refined. Finally, our study made it possible to reveal the presence of some specific relationships between these five classes and the microphysics of their associated rain events.

scholarly journals Application of Kohonen networks for clustering of the Zachodniopomorskie Voivodeship districts in terms of the level of socio-economic development

2020 ◽  
Vol 64 (9) ◽  
pp. 100-118
Author(s):  
Joanna Perzyńska
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Economic Development ◽  
Statistical Data ◽  
Multidimensional Analysis ◽  
Diagnostic Features ◽  
Self Organizing Maps ◽  
Socio Economic Development ◽  
Kohonen Networks ◽  
Theoretical Considerations

The author presents the possibilities of using artificial neural networks in a multidimensional analysis – cluster analysis. The empirical example using districts of the Zachodniopomorskie (West Pomeranian) Voivodeship is the illustration of theoretical considerations. The study used statistical data from many areas related to socio-economic development: demography, labour market, natural environment, recreation, culture, social and technical infrastructure, and the economy. The aim of the study was to divide the voivodeship into disjointed typological groups of districts using Kohonen networks (Self-Organizing Maps). Several networks differing in structure of the output layer were constructed and trained. Selected diagnostic features of socio-economic development of districts were their input values. Using verified Kohonen networks, various sets of groups of the researched objects were created, and confirmed them are a useful tool for identifying clusters of districts similar to each other in terms of the level of socio-economic development.

scholarly journals Growing Self-Organizing Maps for Data Analysis

2011 ◽  
pp. 781-787
Author(s):  
Soledad Delgado ◽  
Consuelo Gonzalo ◽  
Estíbaliz Martínez ◽  
Águeda Arquero
Keyword(s):  
Dimensional Space ◽  
Simulated Data ◽  
Multidimensional Data ◽  
Self Organizing Maps ◽  
Research Areas ◽  
Multidimensional Datasets ◽  
Cell Structures ◽  
Growing Cell ◽  
Growing Cell Structures ◽  
Self Organizing

Currently, there exist many research areas that produce large multivariable datasets that are difficult to visualize in order to extract useful information. Kohonen selforganizing maps have been used successfully in the visualization and analysis of multidimensional data. In this work, a projection technique that compresses multidimensional datasets into two dimensional space using growing self-organizing maps is described. With this embedding scheme, traditional Kohonen visualization methods have been implemented using growing cell structures networks. New graphical map displays have been compared with Kohonen graphs using two groups of simulated data and one group of real multidimensional data selected from a satellite scene.

scholarly journals Multidimensional Deprivation in Contemporary Switzerland across Social Groups and Time

2014 ◽  
Vol 19 (1) ◽  
pp. 42-55 ◽  
Author(s):  
Mario Lucchini ◽  
Christine Butti ◽  
Jenny Assi ◽  
Dario Spini ◽  
Laura Bernardi
Keyword(s):  
Topological Space ◽  
Transition Probabilities ◽  
Dimensional Space ◽  
Social Groups ◽  
Well Being ◽  
Financial Vulnerability ◽  
Dynamic Approach ◽  
Self Organizing Maps ◽  
Multidimensional Deprivation ◽  
Som Model

We have investigated the phenomenon of deprivation in contemporary Switzerland through the adoption of a multidimensional, dynamic approach. By applying Self Organizing Maps (SOM) to a set of 33 non-monetary indicators from the 2009 wave of the Swiss Household Panel (SHP), we identified 13 prototypical forms (or clusters) of well-being, financial vulnerability, psycho-physiological fragility and deprivation within a topological dimensional space. Then new data from the previous waves (2003 to 2008) were classified by the SOM model, making it possible to estimate the weight of the different clusters in time and reconstruct the dynamics of stability and mobility of individuals within the map. Looking at the transition probabilities between year t and year t+1, we observed that the paths of mobility which catalyze the largest number of observations are those connecting clusters that are adjacent on the topological space.

scholarly journals Computing the Discrete Compactness of Orthogonal Pseudo-Polytopes via Their D-EVM Representation

2010 ◽  
Vol 2010 ◽  
pp. 1-28 ◽  
Author(s):  
Ricardo Pérez-Aguila
Keyword(s):  
Dimensional Space ◽  
Efficient Algorithms ◽  
Video Sequences ◽  
Higher Dimensional ◽  
Discrete Compactness ◽  
Representation Scheme ◽  
2D And 3D ◽  
Orthogonal Polytopes

This work is devoted to present a methodology for the computation of Discrete Compactness in -dimensional orthogonal pseudo-polytopes. The proposed procedures take in account compactness' definitions originally presented for the 2D and 3D cases and extend them directly for considering the D case. There are introduced efficient algorithms for computing discrete compactness which are based on an orthogonal polytopes representation scheme known as the Extreme Vertices Model in the -Dimensional Space (D-EVM). It will be shown the potential of the application of Discrete Compactness in higher-dimensional contexts by applying it, through EVM-based algorithms, in the classification of video sequences, associated to the monitoring of a volcano's activity, which are expressed as 4D orthogonal polytopes in the space-color-time geometry.

scholarly journals Mitochondrial event localiser (MEL) to quantitativelydescribe fission, fusion and depolarisation in the three-dimensional space

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0229634
Author(s):  
Rensu P. Theart ◽  
Jurgen Kriel ◽  
André du Toit ◽  
Ben Loos ◽  
Thomas R. Niesler
Keyword(s):  
Dimensional Space ◽  
Mitochondrial Fission ◽  
Three Dimensional ◽  
Time Lapse ◽  
Event Analysis ◽  
Representative Cell ◽  
Before And After ◽  
Cellular Context ◽  
Pre Treatment ◽  
And Control

Mitochondrial fission and fusion play an important role not only in maintaining mitochondrial homeostasis but also in preserving overall cellular viability. However, quantitative analysis based on the three-dimensional localisation of these highly dynamic mitochondrial events in the cellular context has not yet been accomplished. Moreover, it remains largely uncertain where in the mitochondrial network depolarisation is most likely to occur. We present the mitochondrial event localiser (MEL), a method that allows high-throughput, automated and deterministic localisation and quantification of mitochondrial fission, fusion and depolarisation events in large three-dimensional microscopy time-lapse sequences. In addition, MEL calculates the number of mitochondrial structures as well as their combined and average volume for each image frame in the time-lapse sequence. The mitochondrial event locations can subsequently be visualised by superposition over the fluorescence micrograph z-stack. We apply MEL to both control samples as well as to cells before and after treatment with hydrogen peroxide (H2O2). An average of 9.3/7.2/2.3 fusion/fission/depolarisation events per cell were observed respectively for every 10 sec in the control cells. With peroxide treatment, the rate initially shifted toward fusion with and average of 15/6/3 events per cell, before returning to a new equilibrium not far from that of the control cells, with an average of 6.2/6.4/3.4 events per cell. These MEL results indicate that both pre-treatment and control cells maintain a fission/fusion equilibrium, and that depolarisation is higher in the post-treatment cells. When individually validating mitochondrial events detected with MEL, for a representative cell for the control and treated samples, the true-positive events were 47%/49%/14% respectively for fusion/fission/depolarisation events. We conclude that MEL is a viable method of quantitative mitochondrial event analysis.

scholarly journals Concise and Accessible Representations for Multidimensional Datasets: Introducing a Framework Based on thenD-EVM and Kohonen Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-21
Author(s):  
Ricardo Pérez-Aguila ◽  
Ricardo Ruiz-Rodríguez
Keyword(s):  
Dimensional Space ◽  
Second Step ◽  
Topological Properties ◽  
Color Intensity ◽  
Topological Information ◽  
Spatial Complexity ◽  
Multidimensional Datasets ◽  
Kohonen Networks ◽  
Orthogonal Polytopes ◽  
New Framework

A new framework intended for representing and segmenting multidimensional datasets resulting in low spatial complexity requirements and with appropriate access to their contained information is described. Two steps are going to be taken in account. The first step is to specify (n-1)D hypervoxelizations,n≥2, as Orthogonal Polytopes whosenth dimension corresponds to color intensity. Then, thenD representation is concisely expressed via the Extreme Vertices Model in then-Dimensional Space (nD-EVM). Some examples are presented, which, under our methodology, have storing requirements minor than those demanded by their original hypervoxelizations. In the second step, 1-Dimensional Kohonen Networks (1D-KNs) are applied in order to segment datasets taking in account their geometrical and topological properties providing a non-supervised way to compact even more the proposedn-Dimensional representations. The application of our framework shares compression ratios, for our set of study cases, in the range 5.6496 to 32.4311. Summarizing, the contribution combines the power of thenD-EVM and 1D-KNs by producing very concise datasets’ representations. We argue that the new representations also provide appropriate segmentations by introducing some error functions such that our 1D-KNs classifications are compared against classifications based only in color intensities. Along the work, main properties and algorithms behind thenD-EVM are introduced for the purpose of interrogating the final representations in such a way that it efficiently obtains useful geometrical and topological information.

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