iFRAME (inhomogeneous Filters Random Field And Maximum Entropy)

Experiment 2: Learning Sparse FRAME by Generative Boosting with Local Normalization

Code


Contents
Case 1 : Cat
Case 2 : Lion
Case 3 : Hummingbird
Case 4 : Tiger

more examples


Case 1: Cat

(1) Training images

(2) Comparison of local normalization using different window sizes (scaleFactor = 0.5, 1, 2, 3, and 4)

Synthesis images

movie 1 | movie 2 | movie 3 | movie 4 | movie 5

Sketch templates

(3) Parameters setting:

General Parameters: nOrient = 16; sizeTemplatex=100; sizeTemplatey=100; GaborScaleList=[ 1.4, 1, 0.7, 0.5]; DoGScaleList =[]; LocationShiftLimit=2; OrientShiftLimit=1; interval=5; #Wavelet=300; nIteration= (interval) x (#Wavelet) x 2;
Local normalization Parameters: isLocalNormalize=true; isSeparate=false; thresholdFactor=0.01; localNormScaleFactor=0.5, 1, 2, 3, or 4;
Gibbs Sampler Parameters: lambdaLearningRate = 0.1/sqrt(sigsq); 6x6 chains; sigsq=10; threshold_corrBB=0; lower_bound_rand = 0.001; upper_bound_rand = 0.999; c_val_list=-30:2:30

Case 2: Lion

(1) Training images

(2) Comparison of local normalization using different window sizes (scaleFactor = 0.5, 1, 2, 3, and 4)

Synthesis images

movie 1 | movie 2 | movie 3 | movie 4 | movie 5

Sketch templates

(3) Parameters setting:

General Parameters: nOrient = 16; sizeTemplatex=100; sizeTemplatey=100; GaborScaleList=[ 1.4, 1, 0.7, 0.5]; DoGScaleList =[]; LocationShiftLimit=2; OrientShiftLimit=1; interval=5; #Wavelet=300; nIteration= (interval) x (#Wavelet) x 2;
Local normalization Parameters: isLocalNormalize=true; isSeparate=false; thresholdFactor=0.01; localNormScaleFactor=0.5, 1, 2, 3, or 4;
Gibbs Sampler Parameters: lambdaLearningRate = 0.1/sqrt(sigsq); 6x6 chains; sigsq=10; threshold_corrBB=0; lower_bound_rand = 0.001; upper_bound_rand = 0.999; c_val_list=-30:2:30

Case 3: Hummingbird

(1) Training images

(2) Comparison of local normalization using different window sizes (scaleFactor = 0.5, 1, 2, 3, and 4)

Synthesis images

movie 1 | movie 2 | movie 3 | movie 4 | movie 5

Sketch templates

(3) Parameters setting:

General Parameters: nOrient = 16; sizeTemplatex=100; sizeTemplatey=100; GaborScaleList=[ 1.4, 1, 0.7, 0.5]; DoGScaleList =[]; LocationShiftLimit=2; OrientShiftLimit=1; interval=5; #Wavelet=300; nIteration= (interval) x (#Wavelet) x 2;
Local normalization Parameters: isLocalNormalize=true; isSeparate=false; thresholdFactor=0.01; localNormScaleFactor=0.5, 1, 2, 3, or 4;
Gibbs Sampler Parameters: lambdaLearningRate = 0.1/sqrt(sigsq); 6x6 chains; sigsq=10; threshold_corrBB=0; lower_bound_rand = 0.001; upper_bound_rand = 0.999; c_val_list=-30:2:30

Case 4: Tiger

(1) Training images

(2) Comparison of local normalization using different window sizes (scaleFactor = 0.5, 1, 2, 3, and 4)

Synthesis images

movie 1 | movie 2 | movie 3 | movie 4 | movie 5

Sketch templates

(3) Parameters setting:

General Parameters: nOrient = 16; sizeTemplatex=100; sizeTemplatey=100; GaborScaleList=[ 1.4, 1, 0.7, 0.5]; DoGScaleList =[]; LocationShiftLimit=2; OrientShiftLimit=1; interval=5; #Wavelet=300; nIteration= (interval) x (#Wavelet) x 2;
Local normalization Parameters: isLocalNormalize=true; isSeparate=false; thresholdFactor=0.01; localNormScaleFactor=0.5, 1, 2, 3, or 4;
Gibbs Sampler Parameters: lambdaLearningRate = 0.1/sqrt(sigsq); 6x6 chains; sigsq=10; threshold_corrBB=0; lower_bound_rand = 0.001; upper_bound_rand = 0.999; c_val_list=-30:2:30