Determining Jigsaw Puzzle State from an Image based on Deep Learning
Published in IEEE International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2022
Abstract
The intriguing nature of jigsaw puzzle has captured the attention of researchers for many years. In this paper, we propose a deep learning model to determine different states of jigsaw puzzle from an image. We represent the task as a classification problem where each state of the puzzle is considered as a class. For this purpose, we have proposed a method to generate a dataset that can efficiently represent the jigsaw puzzle states space. The proposed model has 93% accuracy on the test dataset. In addition, we have shown that when the tiles size changes the model is still able to recognize 83% of the states. Though, genetic algorithms (GA) have been successful in solving larger puzzles, they require hand-crafted sophisticated compatibility scores. The computation and memory requirement to store the piecewise compatibility measure increases with the size of the puzzle. As an application, we have shown that the proposed method can be used as a fitness function of GA based jigsaw puzzle solver without using any compatibility measure.