SVM classification
2016-08-23
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In this project we have 240 images containing 120 beach and 120
grassland photos for classifying using SVM.
I extracted 81 features for each image as follows:
I converted the image from RGB to HSV color space by rgb2hsv.
Uniformly divide the image into 33 blocks by Image2Block function
in my application.
For each of these 9 blocks I calculated 3 values for each of H/S/V
channels of pixels of the blocks. In the application I dened 3 functions
that calculate 3 values Mean,Variance,Skewness for each HSV channel
in each block e.g. the function cal mean gets one block matrix then
returns 3 means of 3 channels of pixels in the given block. The 2 other
functions are cal var and cal skewness to calculate the variance and
skewness of each block.
grassland photos for classifying using SVM.
I extracted 81 features for each image as follows:
I converted the image from RGB to HSV color space by rgb2hsv.
Uniformly divide the image into 33 blocks by Image2Block function
in my application.
For each of these 9 blocks I calculated 3 values for each of H/S/V
channels of pixels of the blocks. In the application I dened 3 functions
that calculate 3 values Mean,Variance,Skewness for each HSV channel
in each block e.g. the function cal mean gets one block matrix then
returns 3 means of 3 channels of pixels in the given block. The 2 other
functions are cal var and cal skewness to calculate the variance and
skewness of each block.
matlab
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