Mineral Texture Identification Using Local Binary Patterns Equipped with a Classification and Recognition Updating System (CARUS)

Saeed Aligholi (), Reza Khajavi, Manoj Khandelwal and Danial Jahed Armaghani
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Saeed Aligholi: Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC 3350, Australia
Reza Khajavi: Earthquake Research Center, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
Manoj Khandelwal: Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC 3350, Australia
Danial Jahed Armaghani: Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76 Lenin Prospect, 454080 Chelyabinsk, Russia

Sustainability, 2022, vol. 14, issue 18, 1-20

Abstract: In this paper, a rotation-invariant local binary pattern operator equipped with a local contrast measure (riLBPc) is employed to characterize the type of mineral twinning by inspecting the texture properties of crystals. The proposed method uses photomicrographs of minerals and produces LBP histograms, which might be compared with those included in a predefined database using the Kullback–Leibler divergence-based metric. The paper proposes a new LBP-based scheme for concurrent classification and recognition tasks, followed by a novel online updating routine to enhance the locally developed mineral LBP database. The discriminatory power of the proposed Classification and Recognition Updating System (CARUS) for texture identification scheme is verified for plagioclase, orthoclase, microcline, and quartz minerals with sensitivity ( TPR ) near 99.9%, 87%, 99.9%, and 96%, and accuracy ( ACC ) equal to about 99%, 97%, 99%, and 99%, respectively. According to the results, the introduced CARUS system is a promising approach that can be applied in a variety of different fields dealing with classification and feature recognition tasks.

Keywords: automated mineral identification; LBP; classification; texture feature (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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