Abstract: The paper describes a self supervised parallel self organizing neural network (PSONN) architecture for true color image segmentation. The proposed architecture is a parallel extension of the standard single self organizing neural network architecture (SONN) and comprises an input (source) layer of image information, three single self organizing neural network architectures for segmentation of the different primary color components in a color image scene and one final output (sink) layer for fusion of the segmented color component images. Responses to the different shades of color components are induced in each of the three single network architectures (meant for component level processing) by applying a multilevel version of the characteristic activation function, which maps the input color information into different shades of color components, thereby yielding a processed component color image segmented on the basis of the different shades of component colors. The number of target classes in the segmented image corresponds to the number of levels in the multilevel activation function. Since the multilevel version of the activation function exhibits several subnormal responses to the input color image scene information, the system errors of the three component network architectures are computed from some subnormal linear index of fuzziness of the component color image scenes at the individual level. Several multilevel activation functions are employed for segmentation of the input color image scene using the proposed network architecture. Results of the application of the multilevel activation functions to the PSONN architecture are reported on three real life true color images. The results are substantiated empirically with the correlation coefficients between the segmented images and the original images.
Abstract: This paper presents a new technique for detection of
human faces within color images. The approach relies on image
segmentation based on skin color, features extracted from the two-dimensional
discrete cosine transform (DCT), and self-organizing
maps (SOM). After candidate skin regions are extracted, feature
vectors are constructed using DCT coefficients computed from those
regions. A supervised SOM training session is used to cluster feature
vectors into groups, and to assign “face" or “non-face" labels to those
clusters. Evaluation was performed using a new image database of
286 images, containing 1027 faces. After training, our detection
technique achieved a detection rate of 77.94% during subsequent
tests, with a false positive rate of 5.14%. To our knowledge, the
proposed technique is the first to combine DCT-based feature
extraction with a SOM for detecting human faces within color
images. It is also one of a few attempts to combine a feature-invariant
approach, such as color-based skin segmentation, together with
appearance-based face detection. The main advantage of the new
technique is its low computational requirements, in terms of both
processing speed and memory utilization.
Abstract: One astonishing capability of humans is to recognize thousands of different objects visually, and to learn the semantic association between those objects and words referring to them. This work is an attempt to build a computational model of such capacity,simulating the process by which infants learn how to recognize objects and words through exposure to visual stimuli and vocal sounds.One of the main fact shaping the brain of a newborn is that lights and colors come from entities of the world. Gradually the visual system learn which light sensations belong to same entities, despite large changes in appearance. This experience is common between humans and several other mammals, like non-human primates. But humans only can recognize a huge variety of objects, most manufactured by himself, and make use of sounds to identify and categorize them. The aim of this model is to reproduce these processes in a biologically plausible way, by reconstructing the essential hierarchy of cortical circuits on the visual and auditory neural paths.
Abstract: We present a non standard Euclidean vehicle
routing problem adding a level of clustering, and we revisit the use
of self-organizing maps as a tool which naturally handles such
problems. We present how they can be used as a main operator
into an evolutionary algorithm to address two conflicting
objectives of route length and distance from customers to bus stops
minimization and to deal with capacity constraints. We apply the
approach to a real-life case of combined clustering and vehicle
routing for the transportation of the 780 employees of an
enterprise. Basing upon a geographic information system we
discuss the influence of road infrastructures on the solutions
generated.
Abstract: Self-organizing map (SOM) is a well known data reduction technique used in data mining. Data visualization can reveal structure in data sets that is otherwise hard to detect from raw data alone. However, interpretation through visual inspection is prone to errors and can be very tedious. There are several techniques for the automatic detection of clusters of code vectors found by SOMs, but they generally do not take into account the distribution of code vectors; this may lead to unsatisfactory clustering and poor definition of cluster boundaries, particularly where the density of data points is low. In this paper, we propose the use of a generic particle swarm optimization (PSO) algorithm for finding cluster boundaries directly from the code vectors obtained from SOMs. The application of our method to unlabeled call data for a mobile phone operator demonstrates its feasibility. PSO algorithm utilizes U-matrix of SOMs to determine cluster boundaries; the results of this novel automatic method correspond well to boundary detection through visual inspection of code vectors and k-means algorithm.
Abstract: In this work we present some matrix operators named
circulant operators and their action on square matrices. This study on
square matrices provides new insights into the structure of the space
of square matrices. Moreover it can be useful in various fields as in
agents networking on Grid or large-scale distributed self-organizing
grid systems.
Abstract: In this paper, we study on color transformation
method on website images for the color blind. The most common
category of color blindness is red-green color blindness which is
viewed as beige color. By transforming the colors of the images, the
color blind can improve their color visibility. They can have a better
view when browsing through the websites. To transform colors on
the website images, we study on two algorithms which are the
conversion techniques from RGB color space to HSV color space and
self-organizing color transformation. The comparative study focuses
on criteria based on the ease of use, quality, accuracy and efficiency.
The outcome of the study leads to enhancement of website images to
meet the color blinds- vision requirements in perceiving image
detailed.
Abstract: Expression data analysis is based mostly on the
statistical approaches that are indispensable for the study of
biological systems. Large amounts of multidimensional data resulting
from the high-throughput technologies are not completely served by
biostatistical techniques and are usually complemented with visual,
knowledge discovery and other computational tools. In many cases,
in biological systems we only speculate on the processes that are
causing the changes, and it is the visual explorative analysis of data
during which a hypothesis is formed. We would like to show the
usability of multidimensional visualization tools and promote their
use in life sciences. We survey and show some of the
multidimensional visualization tools in the process of data
exploration, such as parallel coordinates and radviz and we extend
them by combining them with the self-organizing map algorithm. We
use a time course data set of transitional cell carcinoma of the bladder
in our examples. Analysis of data with these tools has the potential to
uncover additional relationships and non-trivial structures.
Abstract: In this paper, a model of self-organizing spiking neural networks is introduced and applied to mobile robot environment representation and path planning problem. A network of spike-response-model neurons with a recurrent architecture is used to create robot-s internal representation from surrounding environment. The overall activity of network simulates a self-organizing system with unsupervised learning. A modified A* algorithm is used to find the best path using this internal representation between starting and goal points. This method can be used with good performance for both known and unknown environments.
Abstract: This research intends to introduce a new usage of Artificial Intelligent (AI) approaches in Stepping Stone Detection (SSD) fields of research. By using Self-Organizing Map (SOM) approaches as the engine, through the experiment, it is shown that SOM has the capability to detect the number of connection chains that involved in a stepping stones. Realizing that by counting the number of connection chain is one of the important steps of stepping stone detection and it become the research focus currently, this research has chosen SOM as the AI techniques because of its capabilities. Through the experiment, it is shown that SOM can detect the number of involved connection chains in Network-based Stepping Stone Detection (NSSD).
Abstract: In the proposed method for Web page-ranking, a
novel theoretic model is introduced and tested by examples of order
relationships among IP addresses. Ranking is induced using a
convexity feature, which is learned according to these examples
using a self-organizing procedure. We consider the problem of selforganizing
learning from IP data to be represented by a semi-random
convex polygon procedure, in which the vertices correspond to IP
addresses. Based on recent developments in our regularization
theory for convex polygons and corresponding Euclidean distance
based methods for classification, we develop an algorithmic
framework for learning ranking functions based on a Computational
Geometric Theory. We show that our algorithm is generic, and
present experimental results explaining the potential of our approach.
In addition, we explain the generality of our approach by showing its
possible use as a visualization tool for data obtained from diverse
domains, such as Public Administration and Education.
Abstract: The ability of the brain to organize information and generate the functional structures we use to act, think and communicate, is a common and easily observable natural phenomenon. In object-oriented analysis, these structures are represented by objects. Objects have been extensively studied and documented, but the process that creates them is not understood. In this work, a new class of discrete, deterministic, dissipative, host-guest dynamical systems is introduced. The new systems have extraordinary self-organizing properties. They can host information representing other physical systems and generate the same functional structures as the brain does. A simple mathematical model is proposed. The new systems are easy to simulate by computer, and measurements needed to confirm the assumptions are abundant and readily available. Experimental results presented here confirm the findings. Applications are many, but among the most immediate are object-oriented engineering, image and voice recognition, search engines, and Neuroscience.
Abstract: Biological data has several characteristics that strongly differentiate it from typical business data. It is much more complex, usually large in size, and continuously changes. Until recently business data has been the main target for discovering trends, patterns or future expectations. However, with the recent rise in biotechnology, the powerful technology that was used for analyzing business data is now being applied to biological data. With the advanced technology at hand, the main trend in biological research is rapidly changing from structural DNA analysis to understanding cellular functions of the DNA sequences. DNA chips are now being used to perform experiments and DNA analysis processes are being used by researchers. Clustering is one of the important processes used for grouping together similar entities. There are many clustering algorithms such as hierarchical clustering, self-organizing maps, K-means clustering and so on. In this paper, we propose a clustering algorithm that imitates the ecosystem taking into account the features of biological data. We implemented the system using an Ant-Colony clustering algorithm. The system decides the number of clusters automatically. The system processes the input biological data, runs the Ant-Colony algorithm, draws the Topic Map, assigns clusters to the genes and displays the output. We tested the algorithm with a test data of 100 to1000 genes and 24 samples and show promising results for applying this algorithm to clustering DNA chip data.
Abstract: Self-organizing map (SOM) provides both clustering and visualization capabilities in mining data. Dynamic self-organizing maps such as Growing Self-organizing Map (GSOM) has been developed to overcome the problem of fixed structure in SOM to enable better representation of the discovered patterns. However, in mining large datasets or historical data the hierarchical structure of the data is also useful to view the cluster formation at different levels of abstraction. In this paper, we present a technique to generate concept trees from the GSOM. The formation of tree from different spread factor values of GSOM is also investigated and the quality of the trees analyzed. The results show that concept trees can be generated from GSOM, thus, eliminating the need for re-clustering of the data from scratch to obtain a hierarchical view of the data under study.
Abstract: The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be preserved. With such characteristics, the SOM was usually applied on data clustering and visualization tasks. However, the SOM has main disadvantage of the need to know the number and structure of neurons prior to training, which are difficult to be determined. Several schemes have been proposed to tackle such deficiency. Examples are growing/expandable SOM, hierarchical SOM, and growing hierarchical SOM. These schemes could dynamically expand the map, even generate hierarchical maps, during training. Encouraging results were reported. Basically, these schemes adapt the size and structure of the map according to the distribution of training data. That is, they are data-driven or dataoriented SOM schemes. In this work, a topic-oriented SOM scheme which is suitable for document clustering and organization will be developed. The proposed SOM will automatically adapt the number as well as the structure of the map according to identified topics. Unlike other data-oriented SOMs, our approach expands the map and generates the hierarchies both according to the topics and their characteristics of the neurons. The preliminary experiments give promising result and demonstrate the plausibility of the method.
Abstract: The competitive learning is an adaptive process in
which the neurons in a neural network gradually become sensitive to
different input pattern clusters. The basic idea behind the Kohonen-s
Self-Organizing Feature Maps (SOFM) is competitive learning.
SOFM can generate mappings from high-dimensional signal spaces
to lower dimensional topological structures. The main features of this
kind of mappings are topology preserving, feature mappings and
probability distribution approximation of input patterns. To overcome
some limitations of SOFM, e.g., a fixed number of neural units and a
topology of fixed dimensionality, Growing Self-Organizing Neural
Network (GSONN) can be used. GSONN can change its topological
structure during learning. It grows by learning and shrinks by
forgetting. To speed up the training and convergence, a new variant
of GSONN, twin growing cell structures (TGCS) is presented here.
This paper first gives an introduction to competitive learning, SOFM
and its variants. Then, we discuss some GSONN with fixed
dimensionality, which include growing cell structures, its variants
and the author-s model: TGCS. It is ended with some testing results
comparison and conclusions.
Abstract: The broadcast problem including the plan design is
considered. The data are inserted and numbered at predefined order
into customized size relations. The server ability to create a full,
regular Broadcast Plan (RBP) with single and multiple channels after
some data transformations is examined. The Regular Geometric
Algorithm (RGA) prepares a RBP and enables the users to catch their
items avoiding energy waste of their devices. Moreover, the
Grouping Dimensioning Algorithm (GDA) based on integrated
relations can guarantee the discrimination of services with a
minimum number of channels. This last property among the selfmonitoring,
self-organizing, can be offered by servers today
providing also channel availability and less energy consumption by
using smaller number of channels. Simulation results are provided.
Abstract: This work presents a neural network model for the
clustering analysis of data based on Self Organizing Maps (SOM).
The model evolves during the training stage towards a hierarchical
structure according to the input requirements. The hierarchical structure
symbolizes a specialization tool that provides refinements of the
classification process. The structure behaves like a single map with
different resolutions depending on the region to analyze. The benefits
and performance of the algorithm are discussed in application to the
Iris dataset, a classical example for pattern recognition.
Abstract: The SOM has several beneficial features which make
it a useful method for data mining. One of the most important
features is the ability to preserve the topology in the projection.
There are several measures that can be used to quantify the goodness
of the map in order to obtain the optimal projection, including the
average quantization error and many topological errors. Many
researches have studied how the topology preservation should be
measured. One option consists of using the topographic error which
considers the ratio of data vectors for which the first and second best
BMUs are not adjacent. In this work we present a study of the
behaviour of the topographic error in different kinds of maps. We
have found that this error devaluates the rectangular maps and we
have studied the reasons why this happens. Finally, we suggest a new
topological error to improve the deficiency of the topographic error.
Abstract: The problem of manipulator control is a highly
complex problem of controlling a system which is multi-input, multioutput,
non-linear and time variant. In this paper some adaptive
fuzzy, and a new hybrid fuzzy control algorithm have been
comparatively evaluated through simulations, for manipulator
control. The adaptive fuzzy controllers consist of self-organizing,
self-tuning, and coarse/fine adaptive fuzzy schemes. These
controllers are tested for different trajectories and for varying
manipulator parameters through simulations. Various performance
indices like the RMS error, steady state error and maximum error are
used for comparison. It is observed that the self-organizing fuzzy
controller gives the best performance. The proposed hybrid fuzzy
plus integral error controller also performs remarkably well, given its
simple structure.