GPU-based SoftAssign for Maximizing Image Utilization in Photomosaics
Author(s): Marcos Slomp, Michihiro Mikamo, Bisser Raytchev, Toru Tamaki, Kazufumi Kaneda.
Article: International Journal of Networking and Computing, Vol. 1, No. 2, pp. 211--229, July,
2011.
[BibTeX] 
Abstract:
Photomosaic generation is a popular non-photorealistic rendering technique, where a single image is assembled from several smaller ones. Visual responses change depending on the
proximity to the photomosaic, leading to many creative prospects for publicity and art. Synthesizing photomosaics typically requires very large image databases in order to produce pleasing
results. Moreover, repetitions are allowed to occur which may locally bias the mosaic. This
paper provides alternatives to prevent repetitions while still being robust enough to work with
coarse image subsets. Three approaches were considered for the matching stage of photomosaics:
a greedy-based procedural algorithm, simulated annealing and SoftAssign. It was found that
the latter delivers adequate arrangements in cases where only a restricted number of images
is available. This paper introduces a novel GPU-accelerated SoftAssign implementation that
outperforms an optimized CPU implementation by a factor of 60 times in the tested hardware.