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 3×3 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.
Image classification with binary gradient contours
BIANCONI, Francesco
2011
Abstract
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 3×3 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
