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GIzMOs: Genuine Image Mosaics with Adaptive Tiling
Author(s) -
Pavić D.,
Ceumern U.,
Kobbelt L.
Publication year - 2009
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2009.01437.x
Subject(s) - tile , image (mathematics) , artificial intelligence , feature (linguistics) , computer vision , computer science , feature detection (computer vision) , set (abstract data type) , matching (statistics) , image processing , pattern recognition (psychology) , mathematics , geography , linguistics , philosophy , statistics , archaeology , programming language
We present a method that splits an input image into a set of tiles. Each tile is then replaced by another image from a large database such that, when viewed from a distance, the original image is reproduced as well as possible. While the general concept of image mosaics is not new, we consider our results as ‘genuine image mosaics’ (or short GIzMOs) in the sense that the images from the database are not modified in any way. This is different from previous work, where the image tiles are usually colour shifted or overlaid with the high‐frequency content of the input image. Besides the regular alignment of the tiles we propose a greedy approach for adaptive tiling where larger tiles are placed in homogenous image regions. By this we avoid the visual periodicity, which is induced by the equal spacing of the image tiles in the completely regular setting. Our overall system addresses also the cleaning of the image database by removing all unwanted images with no meaningful content. We apply differently sophisticated image descriptors to find the best matching image for each tile. For aesthetic and artistic reasons we classify each tile as ‘feature’ or ‘non‐feature’ and then apply a suitable image descriptor. In a user study we have verified that our descriptors lead to mosaics that are significantly better recognizable than just taking, e.g. average colour values.