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About the Author: Witold Pedrycz Department of Electrical and Computer Engineering, University of Alberta, Canada Professor & Canada Research Chair & IEEE Fellow & Professional Engineer Research interests and activities: Software Engineering System modelling and knowledge discovery Reconfigurable and evolvable architectures. Pattern recognition Personal Homepage http://www.ece.ualberta.ca/~pedrycz/index.html

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About the Author: Marek Reformat A member of the IEEE and ACM. A member of program committees of several conferences related to computational intelligence and software engineering. Actively involved in North American Fuzzy Information Processing Society (NAFIPS). Research interests and activities: Knowledge extraction and knowledge representation Semantic-based intelligent systems Decision support Software quality and maintenance Personal Homepage http://www.ece.ualberta.ca/~reform/index.html

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Contents Main process Local descriptors Gabor filter with LBP Similarity measures & Experimental results

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Taxonomy On pixel LBP,CS,RI Psychological WLD Ternary LTP,DLTP Distance based TPLBP,FPLBP Local descriptors Rotation invar Shifted LBP Selete subset U2,DLBP,SELBP On averaged ILBP,MBLBP,GR AB Three Dimen VLBP,LBP-TOP MultiresolutionGa bor,MB,GRAB local descriptors on Gabor filtered image Gabor magnitude LGBP,MHLVP,LGBPHS,MU LGBP Gabor magnitude & phase ELGBP,MBP 3D GV- LBPTOP Gabor phase quantization HGPP,LGPDP,LGXP Derivative patterns ELGBP,LDP

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Main process 1 2 3

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Main process Local pattern: pixel level description

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Contents Main process Local descriptors Gabor filter with LBP Similarity measures & Experimental results

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Local binary patterns: Circular LBP Bilinear interpolation of a pixel Basic LBP 57 Uniform-Ri LBP B A Elongated LBP New Variants Dominant LBP Statistically Effective LBP Hamming LBP Baisc LBP etc

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Local binary patterns: Threshold 100.11 ILBP

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Local binary patterns: Threshold L2 L3L4 Magnitude L1 Sign ELBP

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Local binary patterns: Magnitude Sign Original image Center gray level Local difference M S clbp_S clbp_M clbp_C clbp_map classifier clbp_Histogram CLBP

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Local binary patterns: DLTP=| LTPU-LTPL|=135-40=95 LTP 5 (AELTP) LTP

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Local binary patterns: Soft-LBP

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Gabor filter with LBP: SILT P [64(1-t) 64(1+t)] t=0.1

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Local binary patterns: Integral image D=4+1-(2+3) MB-LBP

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Local binary patterns: GRAB(General Region Assigned to binary) 5 GARB # noise & variations & rotation tolerant operator solving the orientation problem small variation in edge angles cause smaller variations in the binary representation

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Local binary patterns: CS-LBP

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Local binary patterns: TP-LBP

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Local binary patterns: FP-LBP

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Local binary patterns: LDP 1 Robust against Gaussian white noise and non-monotonic illumination changes 2 Rotation invariant

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Local binary patterns: VLBP

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Local binary patterns: VLBP

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Local binary patterns: LBP-TOP

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Local binary patterns: LBP-TOP

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Local binary patterns: LBP-TOP

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Local binary patterns: LBP-TOP

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Contents Main process Local descriptors Gabor filter with LBP Similarity measures & Experimental results

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Gabor filter with LBP

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Gabor filter with LBP

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= = + +

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Gabor filter with LBP Gabor Dennis Gabor, 1946

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Gabor filter with LBP: 10 GGPP 80 GGPP HGP P

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Gabor filter with LBP: GGPP LGPP HGP P

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Gabor filter with LBP: LGPD P

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Gabor filter with LBP: LGXP

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Contents Main process Local descriptors Gabor filter with LBP Similarity measures & Experimental results

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Similarity measures

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Fusing sub-region 1: All sub-regional histograms concatenated 2: Sub-regions of two images are compared pair-wise and the results are aggregated

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Fusing sub-region I2I2 I1I1 I1I1 I2I2 LGX P LGBP_mag A:Feature level fusion

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Fusing sub-region LGBP_mag I1I1 I2I2 I2I2 I1I1 LGXP Others measure: 1:cosine distance measure 2:LDA 1: AdaBoost 2: Borda count Reduction dimensionality

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Thank you