Abstract:
A new approach for image processing, dubbed SOM-QE, that exploits the quantization error (QE) from self-organizing maps (SOM) is proposed in this thesis.
SOM produce low-dimensional discrete representations of high-dimensional input data. QE is determined from the results of the unsupervised learning process
of SOM and the input data. SOM-QE from a time-series of images can be used as
an indicator of changes in the time series. To set-up SOM, a map size, the neighbourhood distance, the learning rate and the number of iterations in the learning
process are determined. The combination of these parameters that gives the lowest value of QE, is taken to be the optimal parameter set and it is used to transform
the dataset. This has been the use of QE. The novelty in SOM-QE technique is
fourfold: first, in the usage. SOM-QE employs a SOM to determine QE for different images - typically, in a time series dataset - unlike the traditional usage where
different SOMs are applied on one dataset. Secondly, the SOM-QE value is introduced as a measure of uniformity within the image. Thirdly, the SOM-QE value
becomes a special, unique label for the image within the dataset and fourthly, this label is used to track changes that occur in subsequent images of the same scene.
Thus, SOM-QE provides a measure of variations within the image at an instance
in time, and when compared with the values from subsequent images of the same
scene, it reveals a transient visualization of changes in the scene of study. In this
research the approach was applied to artificial, medical and geographic imagery to
demonstrate its performance. Changes that occur in geographic scenes of interest,
such as new buildings being put up in a city or lesions receding in medical images are of interest to scientists and engineers. The SOM-QE technique provides
a new way for automatic detection of growth in urban spaces or the progressions
of diseases, giving timely information for appropriate planning or treatment. In
this work, it is demonstrated that SOM-QE can capture very small changes in
images. Results also confirm it to be fast and less computationally expensive in
discriminating between changed and unchanged contents in large image datasets.
Pearson’s correlation confirmed that there was statistically significant correlations
between SOM-QE values and the actual ground truth data. On evaluation, this
technique performed better compared to other existing approaches. This work is
important as it introduces a new way of looking at fast, automatic change detection
even when dealing with small local changes within images. It also introduces a
new method of determining QE, and the data it generates can be used to predict
changes in a time series dataset.
