Novel Learning-Based Visual Algorithms and Fusion Methods for High-Dimensional/Multi-Modality Big Data

点击翻译按钮获取中文摘要

During the last 10 years we have witnessed the immense growth of machine learning (ML) and deep learning (DL) due to the collection of vast amounts of labeled data and the ever-continuing innovation of high-performance computers (HPCs). ML and DL have dramatically become an integral part of our lives and are capable of performing tasks with precision like never before. They, in conjunction with computer vision, have become the cornerstone of a multitude of applications, including customs and border protection (CBP), intelligent transportation systems (ITS), video surveillance, vision-context applications, remote sensing and geographic information systems (GIS), biomedical image analysis, bioinformatics, and information security. The performance of many algorithms in real-world applications depends largely on a strong representation of their input streams. In most cases, unimodal feature representation will be biased and inadequate for a certain learning task, while the multi-modal feature learning frameworks can overcome the aforesaid shortcomings through learning complementary clues. However, the combined feature vector of multi-model systems often lies in a high- dimensional space, posing a serious problem in the final pattern recognition. At the same time, such high-dimensional feature vectors can include redundant components and noise. Thus, it is crucial to develop novel learning-based modeling technologies and fusion methods to remove redundancy and noise and handle complex big data. This proposal focuses on designing and developing hierarchical representation learning models and fusion methods for big data analysis, e.g., multi-modal medical records, static images, time/frequency-domain signals, and dynamic video streams. Specifically, the representation learning strategies will be developed based on machine/deep learning and other relevant models, e.g., non-iterative Moore-Penrose inverse (MPI) networks comprised of random vector functional links (RVFL) and Deep Convolutional Neural Networks (DCNNs). We believe that the research outcomes of the proposed themes will have a significant impact on the academic, healthcare, research, and adjacent industries in Canada and around the world as well in the related fields. The knowledge transfer of this proposal will be carried out through the dissemination of publications in international conferences, journals, books/book chapters, and the PI's active industrial collaborations. At the same time, the HQPs involved in this project will become leaders with cutting-edge expertise in academia as well as related industries through world-class training in the areas of machine learning, computer vision, and image/biomedical image processing and analysis under an interdisciplinary setup.