Dictionary learning methods for computer vision
Abstract
Sparse and redundant signal representations have recently gained much interest in image understanding. This is partly due to the fact that signals or images of interest are often sparse in some dictionary. These dictionaries can be either analytic or they can be learned directly from the data. In fact, it has been observed that learning a dictionary directly from data often leads to improved results in many practical applications such as classification and restoration. In this talk I will give a general overview of dictionary learning methods and talk in detail about my recent work on semi-supervised dictionary learning and non-linear supervised dictionary learning methods.
This talk is organized by Sameh Khamis