Cognitive Modelling and Learning for Multimedia Mining and Understanding
Cognitive modelling and learning have become a new trend for advanced signal analysis, especially for semantic content extraction and understanding. Various approaches have been proposed in recent years to address a range of underlying challenges, including data acquisition, denoising, feature extraction, dimensionality reduction, restoration, data compression, segmentation, detection and classification [1, 2] In addition, fusion and big data mining is also receiving growing attention for enhanced modelling and analysis. With rapid developments in machine learning, signal processing and big data analysis techniques, in particular compressed sensing, deep learning and multi kernel based modelling, there are exciting new opportunities for exploiting these advances for semantic signal analysis and understanding in a range of inter disciplinary research areas. Relevant applications can currently be found in fields ranging from communications, energy and manufacturing to health, security, remote sensing and numerous other fields. As a result, it is timely to summarise recent progress and advancements, including new models, algorithms and innovative applications, particularly those that are focussed on scalability, quality, efficiency and efficacy of solutions. In light of this, this special issue aims to solicit state of theart contributions and also provide a premier forum for both academic and industrial research community to report progress, exchange findings and facilitate multidisciplinary research works. The focus of the special issue covers not only fundamental models, algorithms, integrated solutions and novel applications but also benchmark data and methods for performance assessment. In this special issue, in total, nine papers are included selected from over thirty submissions. Although the topics of these papers are quite diverse, we have found a strong trend of the continuous blooming in deep learning based cognitive modelling and learning, along with hybrid approaches and a wide range of applications. These can be briefly summarised as follows:
Cognitive Computation