What does a convolutional neural network recognize in the moon?
This is an incremental study exploring how CNNs interpret ambiguous patterns like lunar maria, with limited practical impact.
The study used a convolutional neural network to evaluate the probabilities of lunar maria patterns being recognized as specific animals (crab, lion, hare), finding that inclusion or exclusion of Mare Frigoris changes the highest probability recognition from lion to hare.
Many people see a human face or animals in the pattern of the maria on the moon. Although the pattern corresponds to the actual variation in composition of the lunar surface, the culture and environment of each society influence the recognition of these objects (i.e., symbols) as specific entities. In contrast, a convolutional neural network (CNN) recognizes objects from characteristic shapes in a training data set. Using CNN, this study evaluates the probabilities of the pattern of lunar maria categorized into the shape of a crab, a lion and a hare. If Mare Frigoris (a dark band on the moon) is included in the lunar image, the lion is recognized. However, in an image without Mare Frigoris, the hare has the highest probability of recognition. Thus, the recognition of objects similar to the lunar pattern depends on which part of the lunar maria is taken into account. In human recognition, before we find similarities between the lunar maria and objects such as animals, we may be persuaded in advance to see a particular image from our culture and environment and then adjust the lunar pattern to the shape of the imagined object.