The segmentation based local Sigma semantic feature points are modeling the semantic objects in the scene. In the traditional image segmentation based on the segmented foreground scene, we combine with the pixel location, color, Gabor and LBP feature [construct covariance descriptor represents the target semantic information, and finally converted into Euclidean space Sigma features, suitable for learning and classification standard to the scene SVM.

test_cov_sigma_noise.m
test_cov.m
sp_progress_bar.m
sp_load_image.m
sp_kmeans.m
sp_dist2.m
S_compute.m
prefilt.m
makeLBPMap.m
L1_layer.m
IsUniform.m
HOGGradient.m
hog_gist.m
hog.m
hist_isect.m
gistGabor.m
gist.m
getLBPFea.m
get_file_paths.m
gabor_rad.m
Example.m
createGabor.m
create_gabors.m
cov_sigma_noise_salt.m
cov_sigma_guass.m
cov_image.m
cov_guass.m
confusionMatrix.m
BinHOGFeature.m
areaROC.m
svmtrain.mexw32
svmpredict.mexw32
libsvmwrite.mexw32
libsvmread.mexw32

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