Projections as visual aids for classification system design.

Dimensionality reduction is a compelling alternative for high-dimensional data visualization. This method provides insight into high-dimensional feature spaces by mapping relationships between observations (high-dimensional vectors) to low (two or three) dimensional spaces. These low-dimensional representations support tasks such as outlier and group detection based on direct visualization. Supervised learning, a subfield of machine learning, is also concerned with observations. A key task in supervised learning consists of assigning class labels to observations based on generalization from previous experience. Effective development of such classification systems depends on many choices, including features descriptors, learning algorithms, and hyperparameters. These choices are not trivial, and there is no simple recipe to improve classification systems that perform poorly. In this context, we first propose the use of visual representations based on dimensionality reduction (projections) for predictive feedback on classification efficacy. Second, we propose a projection-based visual analytics methodology, and supportive tooling, that can be used to improve classification systems through feature selection. We evaluate our proposal through experiments involving four datasets and three representative learning algorithms

Visualizing the Hidden Activity of Artificial Neural Networks.

In machine learning, pattern classification assigns high-dimensional vectors (observations) to classes based on generalization from examples. Artificial neural networks currently achieve state-of-the-art results in this task. Although such networks are typically used as black-boxes, they are also widely believed to learn (high-dimensional) higher-level representations of the original observations. In this paper, we propose using dimensionality reduction for two tasks: visualizing the relationships between learned representations of observations, and visualizing the relationships between artificial neurons. Through experiments conducted in three traditional image classification benchmark datasets, we show how visualization can provide highly valuable feedback for network designers. For instance, our discoveries in one of these datasets (SVHN) include the presence of interpretable clusters of learned representations, and the partitioning of artificial neurons into groups with apparently related discriminative roles

A Conceptual Modelling Approach to Visualising Linked Data

Increasing numbers of Linked Open Datasets are being published, and many possible data visualisations may be appropriate for a user's given exploration or analysis task over a dataset. Users may therefore find it difficult to identify visualisations that meet their data exploration or analyses needs. We propose an approach that creates conceptual models of groups of commonly used data visualisations, which can be used to analyse the data and users' queries so as to automatically generate recommendations of possible visualisations. To our knowledge, this is the first work to propose a conceptual modelling approach to recommending visualisations for Linked Data

Comparison of curve and surface skeletonization methods for voxel shapes

Surface and curve skeletons are important shape descriptors with applications in shape matching, simplification, retrieval, and animation. In recent years, many surface and curve skeletonization methods for 3D shapes have been proposed. However, practical comparisons of such methods against each other and against given quality criteria are quite limited in the literature. In this paper, we compare 4 surface and 6 recent curve skeletonization methods that operate on voxel shapes. We first compare the selected methods from a global perspective, using the quality criteria established by a reference paper in the field. Next, we propose a detailed comparison that refines the gained insights by highlighting small-scale differences between skeletons obtained by different methods. Keywords: Medial axes; Surface and curve skeletons; Voxel shape

Part-based segmentation by skeleton cut space analysis

\u3cp\u3eWe present a new method for part-based segmentation of voxel shapes that uses medial surfaces to define a segmenting cut at each medial voxel. The cut has several desirable properties–smoothness, tightness, and orientation with respect to the shape’s local symmetry axis, making it a good segmentation tool. We next analyze the space of all cuts created for a given shape and detect cuts which are good segment borders. Our method is robust to noise, pose invariant, independent on the shape geometry and genus, and is simple to implement. We demonstrate our method on a wide selection of 3D shapes.\u3c/p\u3

Tolerance-based feature transforms : a quantitative comparison

Tolerance-based feature transforms (TFTs) assign to each pixel in an image not only the nearest feature pixels on the boundary (origins), but all origins from the minimum distance up to a user-defined tolerance. In this paper, we compare four simple-to-implement methods for computing TFTs on binary images. Of these methods, the Fast Marching TFT and Euclidean TFT are new. The other two extend existing distance transform algorithms. We quantitatively and qualitatively compare all algorithms on speed and accuracy of both distance and origin results. Our analysis is aimed at helping practitioners in the field to choose the right method for given accuracy and performance constraints

Computing refined skeletal features from medial point clouds

Medial representations have been widely used for many shape analysis and processing tasks. Large and complex 3D shapes are, in this context, a challenging case. Recently, several methods have been proposed that extract point-based medial surfaces with high accuracy and computational scalability. However, the resulting medial clouds are of limited use for shape processing due to the difficulty of computing refined medial features from such clouds. In this paper, we show how to bridge the gap between having a raw medial cloud and enriching this cloud with feature points, medial-point classification, medial axis decomposition into sheets, robust regularization, and Y-network extraction. We further show how such properties can be used to support several shape processing sample applications including edge detection and shape segmentation, for a wide range of complex 3D shapes. Keywords: Feature extraction; Shape segmentation; Point cloud; Skeleton segmentation; Polygona