Complementary Methods for Human Visual Perception of Visual Weather Lore Sky Objects Using Machine Learning Methods

Recent research indicate a surge in the use of machine learning and artificial intelligence to compliment the processes of human visual perception. In particular, applying closeness measures of digital objects is of great significance in the attempts to account for the correspondence between digitized sky objects and some human identifiable object. The scoring of computerized objects can be based on testing a combination of well-known features humans use for visual perception, with a consideration that the human visual cognition system is well tailored for discriminating structural information from visual objects. This way, benchmark tests can be used to compute some proximity of detected objects to the specified object’s reality. Apart from producing outputs for use in the predictions, object similarity tests can also act as a mechanism for quality assessment process for the results of computer object detectors. One assumption here is that similar objects cannot qualify as perfect matches to their real objects but may contain some acceptable divergence in their closeness. In this paper, algorithms for extracting shape, color and texture information in visual sky (specific to traditional weather lore) objects are investigated as candidates for visual sky objects benchmarking, and their performances compared using a collection of positive/negative instances of visual sky objects. The rationale for testing both positive/negative instances was due to the fact that while the sky objects detectors can be expected to generate positive detections, the number of false positives detectable should be negligible.