Pattern Recognition in High-Dimensional Data

Vast amounts of data are produced all the time. Yet this data does not easily equate to useful information: extracting information from large amounts of high dimensional data is nontrivial. People are simply drowning in data. A recent and growing source of high-dimensional data is hyperspectral imaging. Hyperspectral images allow for massive amounts of spectral information to be contained in a single image. In this thesis, a robust supervised machine learning algorithm is developed to efficiently perform binary object classification on hyperspectral image data by making use of the geometry of Grassmann manifolds. This algorithm can consistently distinguish between a large range of even very similar materials, returning very accurate classification results with very little training data. When distinguishing between dissimilar locations like crop fields and forests, this algorithm consistently classifies more than 95 percent of points correctly. On more similar materials, more than 80 percent of points are classified correctly. This algorithm will allow for very accurate information to be extracted from these large and complicated hyperspectral images

Optimal Clustering Framework for Hyperspectral Band Selection

Band selection, by choosing a set of representative bands in hyperspectral image (HSI), is an effective method to reduce the redundant information without compromising the original contents. Recently, various unsupervised band selection methods have been proposed, but most of them are based on approximation algorithms which can only obtain suboptimal solutions toward a specific objective function. This paper focuses on clustering-based band selection, and proposes a new framework to solve the above dilemma, claiming the following contributions: 1) An optimal clustering framework (OCF), which can obtain the optimal clustering result for a particular form of objective function under a reasonable constraint. 2) A rank on clusters strategy (RCS), which provides an effective criterion to select bands on existing clustering structure. 3) An automatic method to determine the number of the required bands, which can better evaluate the distinctive information produced by certain number of bands. In experiments, the proposed algorithm is compared to some state-of-the-art competitors. According to the experimental results, the proposed algorithm is robust and significantly outperform the other methods on various data sets

Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports

Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports

Robust Path-based Image Segmentation Using Superpixel Denoising

Clustering is the important task of partitioning data into groups with similar characteristics, with one category being spectral clustering where data points are represented as vertices of a graph connected by weighted edges signifying similarity based on distance. The longest leg path distance (LLPD) has shown promise when used in spectral clustering, but is sensitive to noisy data, therefore requiring a data denoising procedure to achieve good performance. Previous denoising techniques have involved identifying and removing noisy data points, however this is not a desirable pre-clustering step for data sets with a specific structure like images. The process of partitioning an image into regions of similar features known as image segmentation can be represented as a clustering problem by defining the vector of intensity and spatial information at each pixel as data point. We therefore propose the method of pre-cluster denoising to formulate a robust LLPD clustering framework. By creating a fine clustering of approximately equal-sized groups and averaging each, a reduced number of data points can be defined that represent the relevant information of the original data set by locally averaging out noise influence. We can then construct a smaller graph representation of the data based on the LLPD between the reduced data points, and identify the spectral embedding coordinates for each reduced point. An out-of-sample extension procedure is then used to compute spectral embedding coordinates at each of the original data points, after which a simple (k-means) clustering is performed to compute the final cluster labels. In the context of image segmentation, computing superpixels provides a nice structure for performing this type of pre-clustering. We show how the above LLPD framework can be carried out in the context of image segmentation, and show that a simple computationally efficient spatial interpolation procedure can be used instead to extend the embedding in a way that yields better segmentation performance with respect to ground truth on a publicly available data set. Similar experiments are also performed using the standard Euclidean distance in place of the LLPD to show the proficiency of the LLPD for image segmentation