The use of large-scale multifaceted data is common in a wide variety of scientific applications. In many cases, this multifaceted data takes the form of a field-based (Eulerian) and point trajectory-based (Lagrangian) representation as each has a unique set of advantages in characterizing a system of study. Furthermore, studying the increasing scale and complexity of these multifaceted datasets is limited by perceptual ability and available computational resources, necessitating sophisticated data reduction and feature extraction techniques. In this work, we present a new 4D feature segmentation extraction scheme that can operate on both the field and point trajectory data types simultaneously. The resulting features are time-varying data subsets that have both a field and point-based component, and were extracted based on underlying patterns from both data types. This enables researchers to better explore both the spatial and temporal interplay between the two data representations and study underlying phenomena from new perspectives. We parallelize our approach using GPU acceleration and apply it to real world multifaceted datasets to illustrate the types of features that can be extracted and explored.
Since then, @cite_26 have gone on to extend the SLIC algorithm to operate on 3D volumes and use supervoxels'' in conjunction with uncertainty-based refinement to extract volume features from large-scale datasets. Such a technique has a great deal of potential, especially if it can be applied in 4D and extended to operate on both field and point-based data types. As a result, we use the ideas originally established in the SLIC algorithm as a starting point in developing our multifaceted 4D feature segmentation and extraction scheme.
@cite_26
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Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels. Despite its simplicity, SLIC adheres to boundaries as well as or better than previous methods. At the same time, it is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.