Non-deep CNN for multi-modal image classification and feature learning: An Azure-based model

Convolutional Neural Networks (CNN) are useful methods for identification of previously unknown embedded patterns in images. Several object and facial recognition along with image segmentation tasks have benefited from the non-linear abstraction of hybrid features using CNN. This work presents a novel CNN model parametrization work-flow developed on the cloud-computing platform of Microsoft Azure Machine Learning Studio (MAMLS) that is capable of learning from the feature maps and classifying multi-modal images with different variabilities using one common flow. This two-step work-flow trains CNN models using 70/30 data split. First, the CNN layers are fixed and the optimal kernel and normalization parameters are identified that maximize classification accuracy on the test data. Next, using the optimal kernel and normalization parameters, the best CNN architecture that maximizes classification accuracy is detected. Finally, the activated feature maps (AFMs) from the optimally parameterized CNN model are analyzed to learn new features that can enhance image-based classification accuracies. The proposed flow achieves classification accuracies in the range of 92.5-99.2% that can be further enhanced by doubling the samples based on the features learned from the AFMs. The proposed non-deep CNN models in the MAMLS platform are capable of processing image data sets with 400-4 million samples using a common flow without exponential increase in the computation time. Thus, optimally parametrized non-deep CNN models are capable of identifying novel features that may enhance image-based classification accuracies.