A probabilistic image jigsaw puzzle solver

We explore the problem of reconstructing an image from a bag of square, non-overlapping image patches, the jigsaw puzzle problem. Completing jigsaw puzzles is challenging and requires expertise even for humans, and is known to be NP-complete. We depart from previous methods that treat the problem as a constraint satisfaction problem and develop a graphical model to solve it. Each patch location is a node and each patch is a label at nodes in the graph. A graphical model requires a pairwise compatibility term, which measures an affinity between two neighboring patches, and a local evidence term, which we lack. This paper discusses ways to obtain these terms for the jigsaw puzzle problem. We evaluate several patch compatibility metrics, including the natural image statistics measure, and experimentally show that the dissimilarity-based compatibility

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Taeg Sang Cho, Shai Avidan, William Freeman
• Keywords: Computer Vision, CVPR 2010, Graphical Model, Jigsaw Puzzle, Jigsaw Puzzle Problem