The quantization error in a Self-Organizing Map as a contrast and colour specific indicator of single-pixel change in large random patterns

Abstract

The quantization error in a fixed-size Self-Organizing Map (SOM) with unsupervised winner-take-all learning has previously been used successfully to detect, in minimal computation time, highly meaningful changes across images in medical time series and in time series of satellite images. Here, the functional properties of the quantization error in SOM are explored further to show that the metric is capable of reliably discriminating between the finest differences in local contrast intensities and contrast signs. While this capability of the QE is akin to functional characteristics of a specific class of retinal ganglion cells (the so-called Y-cells) in the visual systems of the primate and the cat, the sensitivity of the QE surpasses the capacity limits of human visual detection. Here, the quantization error in the SOM is found to reliably signal changes in contrast or colour when contrast information is removed from or added to the image, but not when the amount and relative weight of contrast information is constant and only the local spatial position of contrast elements in the pattern changes. While the RGB Mean reflects coarser changes in colour or contrast well enough, the SOM-QE is shown to outperform the RGB Mean in the detection of single-pixel changes in images with up to five million pixels. This could have important implications in the context of unsupervised image learning and computational building block approaches to large sets of image data (big data), including deep learning blocks, and automatic detection of contrast change at the nanoscale in Transmission or Scanning Electron Micrographs (TEM, SEM), or at the subpixel level in multispectral and hyper-spectral imaging data.

Cited by in corpus (8)

• Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review
• Seven properties of self-organization in the human brain
• Occams Razor for Big Data? On Detecting Quality in Large Unstructured Datasets
• Sensors for expert grip force profiling: towards benchmarking manual control of a robotic device for surgical tool movements
• Pixel precise unsupervised detection of viral particle proliferation in cellular imaging data
• Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map
• Combining visual contrast information with sound can produce faster decisions
• Correlated Effects of Relative Size and Depth in the Perceptual Organization of Multiple Figure-Ground Configurations