Publications

@article{garcia-olalla_olivera_boosting_2019,

title = {Boosting texture-based classification by describing statistical information of gray-levels differences},

author = {Óscar García-Olalla Olivera and Laura Fernández-Robles and Enrique Alegre and Manuel Castejón-Limas and Eduardo Fidalgo},

url = {https://www.mdpi.com/1424-8220/19/5/1048},

year  = {2019},

date = {2019-01-01},

journal = {Sensors},

volume = {19},

number = {5},

pages = {1048},

abstract = {This paper introduces a new texture descriptor booster, CLOSIB (Complete Local Oriented Statistical Information Booster), designed to enhance the discriminative power of texture descriptors like LBP. By using statistical information from image gray-level differences, the method improves texture classification. Variants such as Half-CLOSIB (H-CLOSIB) and Multi-scale CLOSIB (M-CLOSIB) offer increased efficiency and robustness. The method was tested on datasets like KTH TIPS, UIUC, USPTex, and JAFFE, showing improved classification accuracy when combined with LBP-based descriptors. Comparisons with recent algorithms show that CLOSIB variants provide competitive results.},

note = {Publisher: MDPI},

keywords = {CLOSIB, Local Binary Patterns, Statistica Information of Gray-Levels Differences, texture classification, texture description, Visual Sensors},

pubstate = {published},

tppubtype = {article}

}