Image classification with binary gradient contours

✍ Scribed by Antonio Fernández; Marcos X. Álvarez; Francesco Bianconi

• Book ID: 103875360
• Publisher: Elsevier Science
• Year: 2011
• Tongue: English
• Weight: 564 KB
• Volume: 49
• Category: Article
• ISSN: 0143-8166
• DOI: 10.1016/j.optlaseng.2011.05.003

✦ Synopsis

In this work we present a new family of computationally simple texture descriptors, referred to as binary gradient contours (BGC). The BGC methodology relies on computing a set of eight binary gradients between pairs of pixels all along a closed path around the central pixel of a 3 Â 3 grayscale image patch. We developed three different versions of BGC features, namely single-loop, double-loop and triple-loop. To quantitatively assess the effectiveness of the proposed approach we performed an ensemble of texture classification experiments over 10 different datasets. The obtained results make it apparent that the single-loop version is the best performer of the BGC family. Experiments also show that the single-loop BGC texture operator outperforms the well-known LBP. Statistical significance of the achieved accuracy improvement has been demonstrated through the Wilkoxon signed rank test.