Small, Medium, and Big Data: Application of Machine Learning Methods to the Solution of Real-World Imaging and Printing Problems

To provide a context for the discussion to follow, I will first briefly discuss the general characteristics of machine learning. Then, I will describe a series of problems that illustrate the successful application of machine learning methods to the solution of problems in the printing and imaging space. These problems range from the development of detailed microscale models for printer behavior; to algorithms for print and image quality assessment; to algorithms for predicting aesthetic quality of fashion photographs; to algorithms for detection and recognition of people in home and office settings. The algorithms take a variety of different forms ranging from linear regression, context-dependent linear regression, and context-dependent linear regression augmented by stochastic sample function generation; to maximum likelihood estimation; to support vector machines; to convolutional neural networks. The size of the data sets used to train these algorithms range from tens of images to tens of thousands of images.

Speaker: Jan Allebach, Purdue Univ.