| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0535433 |
| utime |
20250123090435.9 |
| mtime |
20201202235959.9 |
| SCOPUS |
85097519102 |
| WOS |
001351026500065 |
| DOI |
10.1007/978-3-030-63007-2_65 |
| title
(primary) (eng) |
Transfer Learning of Mixture Texture Models |
| specification |
| page_count |
13 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0535432 |
| ISBN |
978-3-030-63006-5 |
| ISSN |
0302-9743 |
| title
|
Computational Collective Intelligence |
| page_num |
825-837 |
| publisher |
| place |
Cham |
| name |
Springer Nature Switzerland AG |
| year |
2020 |
|
| editor |
|
| editor |
|
| editor |
|
| editor |
|
| editor |
|
| editor |
|
|
| keyword |
Texture modeling |
| keyword |
transfer learning |
| keyword |
compound random field model |
| keyword |
bidirectional texture function |
| author
(primary) |
| ARLID |
cav_un_auth*0101093 |
| name1 |
Haindl |
| name2 |
Michal |
| institution |
UTIA-B |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept (eng) |
Department of Pattern Recognition |
| department (cz) |
RO |
| department (eng) |
RO |
| full_dept |
Department of Pattern Recognition |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101100 |
| name1 |
Havlíček |
| name2 |
Vojtěch |
| institution |
UTIA-B |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept |
Department of Pattern Recognition |
| department (cz) |
RO |
| department |
RO |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA19-12340S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0376011 |
|
| abstract
(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. |
| action |
| ARLID |
cav_un_auth*0400273 |
| name |
International Conference on Computational Collective Intelligence 2020 /12./ |
| dates |
20201130 |
| mrcbC20-s |
20201203 |
| place |
Da Nang |
| country |
VN |
|
| RIV |
BD |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20205 |
| reportyear |
2021 |
| num_of_auth |
2 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0314144 |
| confidential |
S |
| arlyear |
2020 |
| mrcbU14 |
85097519102 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
001351026500065 WOS |
| mrcbU63 |
cav_un_epca*0535432 Computational Collective Intelligence 978-3-030-63006-5 0302-9743 1611-3349 825 837 Cham Springer Nature Switzerland AG 2020 Lecture Notes in Artificial Intelligence 12496 |
| mrcbU67 |
340 Nguyen N. T. |
| mrcbU67 |
340 Hoang B. H. |
| mrcbU67 |
340 Huynh C. P. |
| mrcbU67 |
340 Hwang D. |
| mrcbU67 |
340 Trawinski B. |
| mrcbU67 |
340 Vossen G. |
|