We present a new neural classification model called Concurrent Self-Organizing Maps (CSOM), representing a winner-takes-all collection of small SOM networks. Each SOM of the system is trained individually to provide best results for one class only. We have considered two significant applications: face recognition and multispectral satellite image classification. For first application, we have u...

We have developed an image retrieval system which uses Tree Structured Self-Organizing Maps (TS-SOMs) as the method for retrieving images similar to a given set of reference images in a database. It also provides a framework for the research on algorithms and methods for content-based retrieval of images. A novel technique introduced in this paper facilitates automatic combination of the respon...

This paper proposes the use of self-organizing maps (SOMs) to the blind source separation (BSS) problem for nonlinearly mixed signals corrupted with multiplicative noise. After an overview of some signal denoising approaches, we introduce the generic independent component analysis (ICA) framework, followed by a survey of existing neural solutions on ICA and nonlinear ICA (NLICA). We then detail...

Self-organizing maps can be used to implement an associative memory for an intelligent system that dynamically learns about new high-level domains over time. SOMs are an attractive option for implementing associative memory: they are fast, easily parallelized, and digest a stream of incoming data into a topographically organized collection of models where more frequent classes of data are repre...

Exploration of large and high-dimensional data sets is one of the main problems in data analysis. Self-organizing maps (SOMs) can be used to map large data sets to a simpler, usually two-dimensional, topological structure. This mapping is able to illustrate dependencies in the data in a very intuitive manner and allows fast location of clusters. However, because of the black-box design of neura...

We have developed a novel system for retrieving images similar to a given set of reference images in large image databases, based on Tree Structured Self-Organizing Maps (TS-SOMs). Our image retrieval system is called PicSOM. It has been designed with the purpose to provide a framework for generic research on algorithms and methods for content-based image retrieval. A new technique introduced i...

In this paper we propose a data stream clustering algorithm, called Self Organizing density based clustering over data Stream (SOStream). This algorithm has several novel features. Instead of using a fixed, user defined similarity threshold or a static grid, SOStream detects structure within fast evolving data streams by automatically adapting the threshold for density-based clustering. It also...

The application of self-organizing maps (SOMs) to the edge detection in biomedical images is discussed. The SOM algorithm has been implemented in MATLAB program suite with various optional parameters enabling the adjustment of the model according to the user’s requirements. For easier application of SOM the graphical user interface has been developed. The edge detection procedure is a critical ...

With the WEBSOM method a textual document collection may be organized onto a graphical map display that provides an overview of the collection and facilitates interactive browsing. Interesting documents can be located on the map using a content-directed search. Each document is encoded as a histogram of word categories which are formed by the self-organizing map (SOM) algorithm based on the sim...

The current standards used for nitrogen pollution evaluation are lacking, and scientific classification methods needed to improve water quality management capabilities. This study addresses the important issue of assessing surface by utilizing two advanced multivariate statistical techniques: self-organizing maps (SOMs) obtained using K-means algorithm Hasse diagram technique (HDT). research ta...