Extended color local mapped pattern for color texture classification under varying illumination

Author(s)

Negri, Tamiris Trevisan ^{[1, 2]} ; Zhou, Fang ^[3] ; Obradovic, Zoran ^[3] ; Gonzaga, Adilson ^[1]

Affiliation

^[1] Univ Sao Paulo, Dept Elect & Comp Engn, Lab Comp Vis, Sao Carlos, SP - Brazil

^[2] Fed Inst Educ Sci & Technol Sao Paulo, Araraquara - Brazil

^[3] Temple Univ, Ctr Data Analyt & Biomed Informat, Philadelphia, PA 19122 - USA

• Document type: Journal article
• Source: JOURNAL OF ELECTRONIC IMAGING; v. 27, n. 1 JAN 2018.
• Web of Science Citations: 1

Abstract

This paper presents a color-texture descriptor based on the local mapped pattern approach for color-texture classification under different lighting conditions. The proposed descriptor, namely extended color local mapped pattern (ECLMP), considers the magnitude of the color vectors inside the RGB cube to extract color-texture information from the images. These features are combined with texture information from the luminance image in a multiresolution fashion to get the ECLMP feature vector. The robustness of the proposed method is evaluated using the RawFooT, KTH-TIPS-2b, and USPtex databases. The experimental results show that the proposed descriptor is more robust to changes in the illumination condition than 22 alternative commonly used descriptors. (c) 2018 SPIE and IS\&T (AU)

• FAPESP's process: 15/20812-5 - Texture descriptors robust to rotation, lighting and colors
• Grantee: Adilson Gonzaga
• Support Opportunities: Regular Research Grants