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.
WANDETO, J (2021). Self-Organizing Map Quantization Error Approach For Detecting Temporal Variations In Image Sets. Afribary. Retrieved from https://tracking.afribary.com/works/self-organizing-map-quantization-error-approach-for-detecting-temporal-variations-in-image-sets
WANDETO, John "Self-Organizing Map Quantization Error Approach For Detecting Temporal Variations In Image Sets" Afribary. Afribary, 13 May. 2021, https://tracking.afribary.com/works/self-organizing-map-quantization-error-approach-for-detecting-temporal-variations-in-image-sets. Accessed 25 Nov. 2024.
WANDETO, John . "Self-Organizing Map Quantization Error Approach For Detecting Temporal Variations In Image Sets". Afribary, Afribary, 13 May. 2021. Web. 25 Nov. 2024. < https://tracking.afribary.com/works/self-organizing-map-quantization-error-approach-for-detecting-temporal-variations-in-image-sets >.
WANDETO, John . "Self-Organizing Map Quantization Error Approach For Detecting Temporal Variations In Image Sets" Afribary (2021). Accessed November 25, 2024. https://tracking.afribary.com/works/self-organizing-map-quantization-error-approach-for-detecting-temporal-variations-in-image-sets