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T n and M n are the values of T and M at the n-th bin Experimental Results Motivation & Applications Completed Local Derivative Pattern for Rotation Invariant Texture Classification Yuting Hu, Zhiling Long, and Ghassan AlRegib Multimedia and Sensors Lab (MSL) at Georgia Tech Georgia Institute of Technology, Atlanta, GA 30332-0250, USA {huyuting, zhiling.long, alregib}@gatech.edu Local Binary Pattern (LBP) Proposed Method Overview Completed Local Derivative Pattern (CLDP) CLDP Histogram Generation CLDP Histogram-based Classification • Circular Sign Pattern • Circular Magnitude Pattern • Directional Derivative Pattern • Global Sign Pattern Sign Magnitude Thresholding Function • Grouping Strategy Rotation-invariant and uniform-2 (“riu2”) for p =8 certain smoothness: 1 (not smooth) 0 (smooth) : the global mean of : the global mean of “_” : Concatenated Histogram “/” : Joint Histogram Chi-square distance between histogram T and M : Nearest neighbor classifier (NNC): The class label of a test image is determined by the training image that has the minimal chi-square distance to the test image. Applications: Rotation-invariant texture classification, texture retrieval, and texture segmentation Motivation: To define a more distinctive and efficient local descriptor that can describe the directional variation of the local differences of two scales. Classification Accuracy (%) (P, R) (8, 2) (8, 3) (16, 2) (8, 3) (24, 2) (24, 3) CLBP_S 77.67 80.94 82.40 85.89 84.07 87.04 CLDP_S/D 86.01 90.69 89.44 92.03 90.18 92.55 ∆ (Accuracy) 8.34 9.75 7.04 6.14 6.11 5.51 CLBP_M 75.45 79.32 79.96 84.35 80.35 85.12 CLDP_M/D 81.81 84.46 84.88 88.56 84.80 89.24 ∆ (Accuracy) 6.36 5.14 4.92 4.21 4.45 4.12 CLBP_M/C 88.03 88.37 91.79 92.36 91.47 93.15 CLDP_M/D/C 91.48 91.42 92.83 94.39 92.37 94.57 ∆ (Accuracy) 3.45 3.05 1.04 2.03 0.90 1.42 CLBP_S_M/C 92.11 92.31 93.41 94.52 93.51 94.94 CLDP_S_D_M/C 92.40 93.87 93.68 95.24 93.69 95.35 ∆ (Accuracy) 0.29 1.56 0.27 0.72 0.18 0.41 CLBP_S/M 92.66 94.54 93.24 95.00 93.40 95.40 CLDP_S/M/D 94.87 96.43 95.07 96.26 93.67 95.59 ∆ (Accuracy) 2.21 1.89 1.83 1.26 0.27 0.10 CLBP_S/M/C 95.41 96.08 95.44 96.16 95.19 96.28 CLDP_S/M/D/C 96.29 97.14 96.25 96.45 94.94 95.97 ∆ (Accuracy) 0.88 1.06 0.81 0.29 -0.25 -0.31 • Average classification accuracy (%) of CLBP and CLDP on TC10 (“inca”) and TC12 (“t184” and “horizon”) using different combination schemes • Classification accuracy (%) of state-of-the-art texture descriptors on TC10 (“inca”) and TC12 (“t184” and “horizon”) Classification Accuracy (%) TC10 INCA TC12 T184 Horizon Average CLBP 99.30 95.32 94.54 94.93 DLBP+NGF 99.10 93.20 90.40 91.80 PRICoLBP g 94.48 92.57 92.50 92.54 LDDP 97.89 95.30 93.40 94.35 CLBC 98.80 94.00 94.07 94.04 DNS+LBP (24,3) 99.27 94.40 93.85 93.63 PLBP_C 98.95 95.32 94.95 95.14 NTLBP 99.24 96.18 94.28 95.23 CLBP_S/M/C (8,1)+(16,2)+(24,3) 99.14 95.18 95.55 95.37 LDDP_1/2 (8,1)+(16,2)+(24,3) 98.64 95.90 94.16 95.08 CLBC_S/M/C (8,1)+(16,2)+(24,3) 99.38 94.98 95.51 95.25 MSJ-LBP 96.53 94.95 96.34 95.65 pi-LBP 99.17 95.72 94.54 95.13 pi-LBP/C 98.96 97.36 97.11 97.24 CLDP (Proposed) 99.32 96.55 95.63 96.09 http://www2.ece.gatech.edu/research/labs/msl/
