In this thesis we explore machine and deep learning approaches that address keychallenges in high dimensional problem areas and also in improving accuracy in wellknown problems. In high dimensional... Show moreIn this thesis we explore machine and deep learning approaches that address keychallenges in high dimensional problem areas and also in improving accuracy in wellknown problems. In high dimensional contexts, we have focused on computational fluid dynamics (CFD) simulations. CFD simulations are able to produce complex and large outputs that accurately describe the physical properties of fluids and gases in various domains and they are frequently used for studying the effects of flow pat-terns and design choices on many engineering designs, such as wing, car and engineshapes. Due to the high dimensional aspect of the data, it is difficult to model to-ward achieving critical goals such as optimizing lift and drag forces. The key research question addressed in this thesis is whether we develop automated approaches that accurately abstract this information? We tackle these issues by studying a closely re-lated field, 3D computer vision, and adapt approaches to the particular data type.Moreover, inspired by this data type we propose new, deep learning, approaches that are also applied to traditional computer vision. Show less
Higher dimensional data such as video and 3D are the leading edge of multimedia retrieval and computer vision research. In this survey, we give a comprehensive overview and key insights into the... Show moreHigher dimensional data such as video and 3D are the leading edge of multimedia retrieval and computer vision research. In this survey, we give a comprehensive overview and key insights into the state of the art of higher dimensional features from deep learning and also traditional approaches. Current approaches are frequently using 3D information from the sensor or are using 3D in modeling and understanding the 3D world. With the growth of prevalent application areas such as 3D games, self-driving automobiles, health monitoring and sports activity training, a wide variety of new sensors have allowed researchers to develop feature description models beyond 2D. Although higher dimensional data enhance the performance of methods on numerous tasks, they can also introduce new challenges and problems. The higher dimensionality of the data often leads to more complicated structures which present additional problems in both extracting meaningful content and in adapting it for current machine learning algorithms. Due to the major importance of the evaluation process, we also present an overview of the current datasets and benchmarks. Moreover, based on more than 330 papers from this study, we present the major challenges and future directions. Show less
