Learning Based Image Analysis - Quality Assessment, Tracking and Classification

This dissertation presents four distinct studies in the fields of image processing and machine learning, focusing on applications ranging from quality assessment for raster images in scanned document and virtual reality facial expression tracking to compression for continual learning and food image classification. First, we shift the traditional focus of image quality assessment (IQA) from natural images to scanned documents, proposing a machine learning-based classification method to evaluate the visual quality of scanned raster images. We enhance the classifier's performance using augmented data generated through noise models simulating scanning degradation. Second, we address the challenges of virtual facial animation in immersive VR, developing a domain adversarial training model to generate domain invariant features and combined it with manifold learning methods for accurate facial action unit (AU) intensity estimation from partially occluded facial images. Third, we explore the use of image compression to increase buffer capacity in continual machine learning systems, thereby enhancing exemplar diversity and mitigating catastrophic forgetting. Our approach includes a new framework that selects compression rate and algorithm, showing significant improvements in image classification accuracy on the CIFAR-100 and ImageNet datasets. Finally, we combine class-activation maps with neural image compression in food image classification systems to adapt to continuously evolving data, extending buffer size and enhancing data diversity, which is validated on food-specific datasets and shows potential for broader applications in continual machine learning systems. Together, these studies demonstrate the versatility of image processing and machine learning techniques in addressing complex and varied challenges across different domains.