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Spatio-temporal grid mining applied to image classification and cellular automata analysis

Abstract : During this thesis, we consider the exhaustive graph mining problem for a special kind of graphs : the grids. Theses grids can be used to model objects that present a regular structure. These structures are naturally present in multiple board games (checkers, chess or go for instance) or in ecosystems models using cellular automata. It is also possible to find this structure in a lower level in images, which are 2D grids of pixels, or even in videos, which are 2D+t spatio-temporal grids of pixels. In this thesis, we proposed a new algorithm to find frequent patterns dedicated to spatio-temporal grids, GriMA. Use of regular grids allow our algorithm to reduce the complexity of the isomorphisms test. These tests are often use by generic graph mining algorithm but because of their complexity, they are rarely used on real data. Two applications were proposed to evaluate our algorithm: image classification for 2D grids mining and prediction of cellular automata for 2D+t grids mining.
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Submitted on : Friday, April 5, 2019 - 10:44:12 AM
Last modification on : Tuesday, June 1, 2021 - 2:08:07 PM


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  • HAL Id : tel-01865020, version 2


Romain Deville. Spatio-temporal grid mining applied to image classification and cellular automata analysis. Data Structures and Algorithms [cs.DS]. Université de Lyon, 2018. English. ⟨NNT : 2018LYSEI046⟩. ⟨tel-01865020v2⟩



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