In this paper we present T1K+, a very large, heterogeneous database of high-quality texture images acquired under variable conditions. T1K+ contains 1129 classes of textures ranging from natural subjects to food, textile samples, construction materials, etc. T1K+ allows the design of experiments especially aimed at understanding the specific issues related to texture classification and retrieval. To help the exploration of the database, all the 1129 classes are hierarchically organized in 5 thematic categories and 266 sub-categories. To complete our study, we present an evaluation of hand-crafted and learned visual descriptors in supervised texture classification tasks.

Cusano, C., Napoletano, P., Schettini, R. (2021). T1k+: A database for benchmarking color texture classification and retrieval methods. SENSORS, 21(3), 1-15 [10.3390/s21031010].

T1k+: A database for benchmarking color texture classification and retrieval methods

Cusano C.;Napoletano P.
;
Schettini R.
2021

Abstract

In this paper we present T1K+, a very large, heterogeneous database of high-quality texture images acquired under variable conditions. T1K+ contains 1129 classes of textures ranging from natural subjects to food, textile samples, construction materials, etc. T1K+ allows the design of experiments especially aimed at understanding the specific issues related to texture classification and retrieval. To help the exploration of the database, all the 1129 classes are hierarchically organized in 5 thematic categories and 266 sub-categories. To complete our study, we present an evaluation of hand-crafted and learned visual descriptors in supervised texture classification tasks.
Articolo in rivista - Articolo scientifico
Color and Texture; Color texture databases; Texture descriptors; Texture features; Texture recognition; Texture retrieval;
English
2-feb-2021
2021
21
3
1
15
1010
none
Cusano, C., Napoletano, P., Schettini, R. (2021). T1k+: A database for benchmarking color texture classification and retrieval methods. SENSORS, 21(3), 1-15 [10.3390/s21031010].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/318878
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