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Open AccessFeature PaperArticle

Partial Order Rank Features in Colour Space

1
School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London E1 4NS, UK
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Department of Engineering, Università degli Studi di Perugia, Via G. Duranti 93, 06125 Perugia, Italy
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Department of Engineering Design, Universidade de Vigo, Rúa Maxwell s/n, 36310 Vigo, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(2), 499; https://doi.org/10.3390/app10020499
Received: 26 November 2019 / Revised: 20 December 2019 / Accepted: 31 December 2019 / Published: 10 January 2020
(This article belongs to the Special Issue Texture and Colour in Image Analysis)
Partial orders are the natural mathematical structure for comparing multivariate data that, like colours, lack a natural order. We introduce a novel, general approach to defining rank features in colour spaces based on partial orders, and show that it is possible to generalise existing rank based descriptors by replacing the order relation over intensity values by suitable partial orders in colour space. In particular, we extend a classical descriptor (the Texture Spectrum) to work with partial orders. The effectiveness of the generalised descriptor is demonstrated through a set of image classification experiments on 10 datasets of colour texture images. The results show that the partial-order version in colour space outperforms the grey-scale classic descriptor while maintaining the same number of features. View Full-Text
Keywords: mathematics of colour and texture; hand-designed image descriptors; rank features; partial orders mathematics of colour and texture; hand-designed image descriptors; rank features; partial orders
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MDPI and ACS Style

Smeraldi, F.; Bianconi, F.; Fernández, A.; González, E. Partial Order Rank Features in Colour Space. Appl. Sci. 2020, 10, 499.

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