Compressive Wavelet Domain Deep CNN for Image Classification Using Genetic Algorithm Based Sensing Mask Learning

Using a novel Genetic Algorithm-based Compressive Learning (GACL), a compressed domain-learning framework is proposed that is implemented on the Haar wavelet approximation coefficient images of the standard kaggle RGB cat dog dataset with every images resized to
$256 \times 256\,\,\times3$
. The compressive sensing (CS) measurements on the selected dataset is achieved by using a simple reduced pixel scheme by retaining only
$\mathrm {P\%}$
of the pixels of the approximation coefficient images and forcing the remaining pixels to 0 using the Primitive Walsh Hadamard (PWH) binary mask and the masked images are used for further learning. A numerical experiment is conducted to analyze the image classification performance of deep convolution neural network (DCNN) learning on compressive sensing (CS) measurements of wavelet approximation coefficient image of the selected dataset. The unmasked wavelet approximation coefficients images are of size only one fourth of the original image, but they visually resembles the original image. The numerical experiment shows that when learning is done on this unmasked wavelet approximation coefficient images a training accuracy of 97% and validation accuracy of 77% are achieved which is remarkable and as good as using the complete spatial domain image. It is found from numerical experiment that, when PWH masking with
$P=10$
is applied only to the test images the validation accuracy falls up to 58% and this fall is due to the fact that the training is done on unmasked images and tested on masked images. On the other hand in a compressive learning framework the DCNN is trained using masked images and when tested using masked images the validation accuracy rises up to 62% due to the fact both the trained images and test images are masked. Further, it is demonstrated that the best PWH masks can be learned by GACL in which the training accuracy increases up to 89% when vertical cross over is used in GACL and increases up to 96% when diagonal crossover is used in GACL. in this case of GACL using diagonal crossover, the 96% of training accuracy using only (10/4 = 2.5) 2.5% of the image is very remarkable and stand as proof of concept to implement compressive learning framework which need very less pixel thus very less measurement rate (MR) both in training and testing phase minimizing the bandwidth and storing requirement in applications related to IoT and cloud solutions. The average SSIM (
$S_{\text {avg}}$
) and average PSNR (
${\text {PSNR}}_{\text {avg}}$
) are used as quality measurements, which reduce as P reduces, and it is demonstrated that the average SSIM and average PSNR improves when GACL is used to learn the mask, which is the key reason for the performance improvement.