  Image classification requires the generation of features capable of detecting
image patterns informative of group identity. The objective of this study was
to classify images from the public CIFAR-10 image dataset by leveraging
combinations of disparate image feature sources from both manual and deep
learning approaches. Histogram of oriented gradients (HOG) and pixel
intensities successfully inform classification (53% and 59% classification
accuracy, respectively), yet there is much room for improvement. VGG16 with
ImageNet trained weights and a CIFAR-10 optimized model (CIFAR-VGG) further
improve upon image classification (60% and 93.43% accuracy, respectively). We
further improved classification by utilizing transfer learning to re-establish
optimal network weights for VGG16 (TL-VGG) and Inception ResNet v2
(TL-Inception) resulting in significant performance increases (85% and 90.74%,
respectively), yet fail to surpass CIFAR-VGG. We hypothesized that if each
generated feature set obtained some unique insight into the classification
problem, then combining these features would result in greater classification
accuracy, surpassing that of CIFAR-VGG. Upon selection of the top 1000
principal components from TL-VGG, TL-Inception, HOG, pixel intensities, and
CIFAR-VGG, we achieved testing accuracy of 94.6%, lending support to our
hypothesis.
