Publication details

Conference Paper (international conference)

Transfer Learning of Mixture Texture Models

Haindl Michal, Havlíček Vojtěch

: Computational Collective Intelligence, p. 825-837 , Eds: Nguyen N. T., Hoang B. H., Huynh C. P., Hwang D., Trawinski B., Vossen G.

: International Conference on Computational Collective Intelligence 2020 /12./, (Da Nang, VN, 20201130)

: GA19-12340S, GA ČR

: Texture modeling, transfer learning, compound random field model, bidirectional texture function

: 10.1007/978-3-030-63007-2_65

: http://library.utia.cas.cz/separaty/2020/RO/haindl-0535433.pdf

(eng): A transfer learning approach for multidimensional parametric mixture random field-based textural representation is introduced. The proposed transfer learning approach allows alleviating the multidimensional mixture models requirement for sufficiently large, but not always available, learning data sets. These compound random field models consist of an underlying structure model that controls transitions between several sub-models, each of them has different characteristics. The structure model proposed is a two-dimensional probabilistic mixture model, either of the Bernoulli or Gaussian mixture type. Local textures are modeled using the fully multispectral three-dimensional Gaussian mixture sub-models. Both presented compound random field models allow the reproduction of, compresses, edits, and enlarges a given measured color, multispectral, or bidirectional texture function (BTF) texture so that ideally, both measured and synthetic textures are visually indiscernible.

: BD

: 20205