Multiple kernel and multi-label learning for image categorization
Serhat S. Bucak · 2014
One crucial step in recovering useful information from large image collections is image categorization. The goal of image categorization is to find the relevant labels for a given image from a closed set of labels. Despite the huge interest and significant contributions by the research community, there remains much room for improvement in the image categorization task. In this dissertation, we develop efficient multiple kernel learning and multi-label learning algorithms with high prediction performance for image categorization. There are many image representation methods available in the literature. However, it is not possible to pick one as the best method for image categorization, since different representations work better in different scenarios. Multiple kernel learning (MKL), a natural extension of ker-nel methods for information fusion, is often used by researchers to improve image representation by integrating it to the learning step for selecting and combining different image features. MKL is mostly considered as a binary classification tool, and it is difficult to scale up MKL when the number of labels is large. We address this computational challenge by developing a stochastic approximation based framework for MKL that aims to learn a single kernel combination that benefits all classes. Another