From Pixels to Titles: Video Game Identification by Screenshots using
  Convolutional Neural Networks
Fabricio Breve
This paper investigates video game identification through single screenshots,
utilizing five convolutional neural network (CNN) architectures (MobileNet,
DenseNet, EfficientNetB0, EfficientNetB2, and EfficientNetB3) across 22 home
console systems, spanning from Atari 2600 to PlayStation 5. Confirming the
hypothesis, CNNs autonomously extract image features, enabling the
identification of game titles from screenshots without additional features.
Using ImageNet pre-trained weights, EfficientNetB3 achieves the highest average
accuracy (74.51%), while DenseNet169 excels in 14 of the 22 systems. Employing
alternative initial weights from another screenshots dataset boosts accuracy
for EfficientNetB2 and EfficientNetB3, with the latter reaching a peak accuracy
of 76.36% and demonstrating reduced convergence epochs from 23.7 to 20.5 on
average. Overall, the combination of optimal architecture and weights attains
77.67% accuracy, primarily led by EfficientNetB3 in 19 systems. These findings
underscore the efficacy of CNNs in video game identification through
screenshots.