Figure 13.

Average feature values for five different classes of OCM image blocks and demonstration of attribute selection. The total dimension of a feature vector is 140. 24 attributes are selected by the information gain based attribute selection algorithm. The selected 24 are marked by red squares along the horizontal axis. Dimension 1–9: ${\mathit{LBP}}_{8,16}^{\mathit{riu}2}$, dimension 10–18: ${\mathit{LBP}}_{8,8}^{\mathit{riu}2}$, dimension 19–27: ${\mathit{LBP}}_{8,4}^{\mathit{riu}2}$, dimension 28–36: ${\mathit{LBP}}_{8,2}^{\mathit{riu}2}$, dimension 37–45: ${\mathit{ALBP}}_{8,16}^{\mathit{riu}2}$, dimension 46–54: ${\mathit{ALBP}}_{8,8}^{\mathit{riu}2}$, dimension 55–63: ${\mathit{ALBP}}_{8,4}^{\mathit{riu}2}$, dimension 64–72: ${\mathit{ALBP}}_{8,2}^{\mathit{riu}2}$, dimension 73–81:SBLBP_8,3, dimension 82–90:SBLBP_8,6, dimension 91–99:SBLBP_8,12, dimension 100–108:SBLBP_8,18, dimension 109–116:RBLBP_8,1,3, dimension 117–124:RBLBP_8,2,3, dimension 125–132:RBLBP_8,4,3, dimension 133–140:RBLBP_8,6,3.