CLASSIFICATION OF DIFFERENT TYPES OF CORONAS USING PARAMETRIZATION OF IMAGES AND MACHINE LEARNING
Igor Kononenko, Matjaz Bevk, Sasa Sadikov, Luka Sajn
University of Ljubljana, Faculty of Computer and Information Science,
Ljubljana, Slovenia
Abstract
We describe the development of computer classifiers for various types of coronas. In particular, we were interested to develop an algorithm for detection of coronas of people in altered states of consciousness (two-classes problem). Such coronas are known to have rings (double coronas),
special branch-like structure of streamers and/or curious spots. Besides detecting altered states of consciousness we were interested also to classify various types of coronas (six-classes problem).
We used several approaches to parametrization of images: statistical approach, principal component analysis, association rules and GDV software approach extended with several additional parameters. For the development of the classifiers we used various machine learning algorithms: learning of decision trees, naïve Bayesian classifier, K-nearest neighbors classifier, Support vector machine, neural networks, and Kernel Density classifier. We compared results of computer algorithms with the human expert’s accuracy (about 77% for the two-classes problem and about 60% for the six-classes problem). Results show that computer algorithms can achieve the same or even better accuracy than that of human experts (best results were up to 85% for the two-classes problem and up to 65% for the six-classes problem).
1. Introduction
Recently developed technology by dr. Korotkov (1998) from Technical University in St.Petersburg, based on the Kirlian effect, for recording the human bio-electromagnetic field (aura) using the Gas Discharge Visualization (GDV) technique provides potentially useful information about the biophysical and/or psychical state of the recorded person. In order to make the unbiased decisions about the state of the person we want to be able to develop the computer algorithm for extracting information/describing/classifying/making decisions about the state of the person from the recorded coronas of fingertips.
The aim of our study is to differentiate 6 types of coronas, 3 types in normal state of consciousness: Ia, Ib, Ic (pictures were recorded with single GDV camera in Ljubljana, all with the same settings of parameters, classification into 3 types was done manually):
Ia – harmonious energy state (120 coronas)
Ib – non-homogenous but still energetically full (93 coronas) Ic – energetically poor (76 coronas)
and 3 types in altered states of consciousness (pictures obtained from dr. Korotkov, recorded by different GDV cameras with different settings of parameters and pictures were not normalized – they were of variable size):
- Rings – double coronas (we added 7 pictures of double coronas recorded in Ljubljana) (90 coronas)
- Branches – long streamers branching in various directions (74 coronas)
Spots – unusual spots (51 coronas)
Our aim is to differentiate normal from altered state of consciousness (2 classes) and to differentiate among all 6 types of coronas (6 classes). Figure 1 provides example coronas for each type.
2. The methodology
We first had to preprocess all the pictures so that all were of equal size (320 x 240). We then described the pictures with various sets of numerical parameters (attributes) with five different parametrization algorithms (described in more detail in the next section):
a) IP (Image Processor – 22 attributes),
b) PCA (Principal Component Analysis),
c) Association Rules,
d) GDV Assistant with some basic GDV parameters,
e)GDV Assistant with additional parameters.
Therefore we had available 5 different learning sets for two-classes problem: altered (one of Rings, Spots, and Branches) versus non- altered (one of Ia, Ib, Ic) state of consciousness. Some of the sets were used also as six-classes problems (differentiating among all six different types of coronas).
We tried to solve some of the above classification tasks by using various machine learning algorithms as implemented in Weka system (Witten and Frank, 2000):
- Quinlan’s (1993) C4.5 algorithm for generating decision trees;
- K-nearest neighbor classifier by Aha, D., and D. Kibler (1991);
- Simple Kernel Density classifier;
- Naïve Bayesian classifier using estimator classes: Numeric estimator precision valuesare chosen based on analysis of the training data. For this reason, the classifier is not an Updateable Classifier (which in typical usage are initialized with zero training instances, see (John and Langley, 1995));
- SMO implements John C. Platt’s sequential minimal optimization algorithm for training a support vector classifier using polynomial kernels. It transforms the output of SVM
Types Ia, Ib and Ic– normal state of consciousness
into probabilities by applying a standard sigmoid function that is not fitted to the data. This implementation globally replaces all missing values and transforms nominal attributes into binary ones (see Platt, 1998; Keerthi et al., 2001);
Neural networks: standard multilayared feedforward neural network with backpropagation of errors learning mechanism (Rumelhart et al., 1986).
SMO algorithm can be used only for two-classes problems, while the other algorithms can be used on two-classes and on six-classes problems.
Types Branches, Rings and Spots– altered states of consciousness
Figure 1: Example coronas for each type.
2004-Kononenko-altered-coronasFull text PDF: 2004-Kononenko-altered-coronas
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