Fractal measures of image local features: an application to texture recognition

Author(s)

Silva, Pedro M. ^[1] ; Florindo, Joao B. ^[2]

Affiliation

^[1] Fed Inst Educ Sci & Technol Espirito Santo, Rodovia Governador Jose Sete 184, BR-29150410 Cariacica, ES - Brazil

^[2] Univ Estadual Campinas, Inst Math Stat & Sci Comp, Rua Sergio Buarque de Holanda 651, BR-13083859 Campinas, SP - Brazil

• Document type: Journal article
• Source: MULTIMEDIA TOOLS AND APPLICATIONS; v. 80, n. 9, p. 14213-14229, APR 2021.
• Web of Science Citations: 0

Abstract

Here we propose a new method for the classification of texture images combining fractal measures (fractal dimension, multifractal spectrum and lacunarity) with local binary patterns. More specifically we compute the box counting dimension of the local binary codes thresholded at different levels to compose the feature vector. The proposal is assessed in the classification of three benchmark databases: KTHTIPS-2b, UMD and UIUC as well as in a real-world problem, namely the identification of Brazilian plant species (database 1200Tex) using scanned images of their leaves. The proposed method demonstrated to be competitive with other state-of-the-art solutions reported in the literature. Such results confirmed the potential of combining a powerful local coding description with the multiscale information captured by the fractal dimension for texture classification. (AU)

• FAPESP's process: 16/16060-0 - Pattern Recognition on Images Based on Complex Systems
• Grantee: Joao Batista Florindo
• Support Opportunities: Regular Research Grants