Neural Network algorithm Learning---Preprocessing of image data 1

An example of image recognition based on convolutional neural network is the preprocessing of input image in common use.

Step1:resize

STEP2: Go to mean value. It should be noted here that the average is calculated for all training sample images, and then the average is subtracted from each sample picture. The test picture is also subtracted from the mean when it is preprocessed (note that it is not the mean of the minus test picture, but the average of all training sample images)

Normalization Processing: Image processing applications generally do not need to be normalized, because the RGB channel data range has been 0~255, is inherently normalized, so there is no need for normalization processing.

PCA dimensionality Reduction Processing: When the data volume is large, it can be reduced by PCA to reduce the computational amount and improve the processing speed. But in the image processing application, because the general first carries on the image resize processing, reduces the data quantity, therefore generally does not need to carry on the PCA dimensionality reduction processing.