In this paper we introduce Improved Opponent Colour Local Binary Patterns (IOCLBP), a conceptually simple yet effective descriptor for colour texture classification. The method was experimentally validated over eight datasets of colour texture images. The results show that IOCLBP outperformed other LBP variants and was at least as effective as last generation features from Convolutional Neural Networks.
Bianconi, F., Bello Cerezo, R., Napoletano, P., & Di Maria, F. (2017). Improved opponent colour local binary patterns for colour texture classification. In Computational Color Imaging (pp.272-281). Springer Verlag [10.1007/978-3-319-56010-6_23].
Citazione: | Bianconi, F., Bello Cerezo, R., Napoletano, P., & Di Maria, F. (2017). Improved opponent colour local binary patterns for colour texture classification. In Computational Color Imaging (pp.272-281). Springer Verlag [10.1007/978-3-319-56010-6_23]. | |
Tipo: | paper | |
Carattere della pubblicazione: | Scientifica | |
Presenza di un coautore afferente ad Istituzioni straniere: | No | |
Titolo: | Improved opponent colour local binary patterns for colour texture classification | |
Autori: | Bianconi, F; Bello Cerezo, R; Napoletano, P; Di Maria, F | |
Autori: | ||
Data di pubblicazione: | 2017 | |
Lingua: | English | |
Nome del convegno: | International Workshop on Computational Color Imaging, CCIW 2017 | |
ISBN: | 9783319560090 | |
Serie: | LECTURE NOTES IN COMPUTER SCIENCE | |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1007/978-3-319-56010-6_23 | |
Appare nelle tipologie: | 02 - Intervento a convegno |