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PHOG: Photometric and geometric functions for textured shape retrieval
Author(s) -
Biasotti S.,
Cerri A.,
Giorgi D.,
Spagnuolo M.
Publication year - 2013
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/cgf.12168
Subject(s) - computer science , geodesic , artificial intelligence , generalization , persistent homology , transformation geometry , computer vision , object (grammar) , selection (genetic algorithm) , pattern recognition (psychology) , mathematics , algorithm , geometry , mathematical analysis
Abstract In this paper we target the problem of textured 3D object retrieval. As a first contribution, we show how to include photometric information in the persistence homology setting, also proposing a novel theoretical result about multidimensional persistence spaces. As a second contribution, we introduce a generalization of the integral geodesic distance which fuses shape and color properties. Finally, we adopt a purely geometric description based on the selection of geometric functions that are mutually independent. The photometric, hybrid and geometric descriptions are combined into a signature, whose performance is tested on a benchmark dataset.