Active Learning for Multimedia Computing: Survey, Recent Trends and Applications

Abstract

The widespread emergence and deployment of inexpensive sensors has resulted in the generation of enormous amounts of digital data in today's world. While this has expanded the possibilities of solving real world problems using computational learning frameworks, selecting the salient data samples from such huge collections of data has proved to be a significant and practical challenge. Further, to train a reliable classification model, it is important to have a large quantity of labeled training data. Manual annotation of large amounts of data is an expensive process in terms of time, labor and human expertise. This has set the stage for research in the field of active learning. Active learning algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and thereby tremendously reduce human annotation effort in training an effective classifier. It can be applied across all existing classification / regression methods and with any kind of data, thus making it a very generalizable approach. The success of active learning in several applications (such as image retrieval, image recognition) has resulted in the extension of the framework to problem settings beyond regular classification / regression. Active learning concepts have been extended to newer problem settings (such as feature selection, video summarization, matrix completion) and have also been combined with other learning paradigms such as deep learning and transfer learning. This tutorial will seek to present a comprehensive overview of active learning with a focus on multimedia computing applications, including historical perspectives, theoretical analysis and novel paradigms. The novelty of this tutorial lies in its focus on the emerging trends, algorithms and applications of active learning. It will aim at introducing concepts and open perspectives that motivate further work in this domain, ranging from fundamentals to applications and systems.