Persistence Bag-of-Words for Topological Data Analysis

Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs). PDs exhibit, however, complex structure and are difficult to integrate in today's machine learning workflows. This paper introduces persistence bag-of-words: a novel and stable vectorized representation of PDs that enables the seamless integration with machine learning. Comprehensive experiments show that the new representation achieves state-of-the-art performance and beyond in much less time than alternative approaches.Comment: Accepted for the Twenty-Eight International Joint Conference on Artificial Intelligence (IJCAI-19). arXiv admin note: substantial text overlap with arXiv:1802.0485

A multi-scale topological shape model for single and multiple component shapes

A novel shape model of multi-scale topological features is proposed which considers those features relating to connected components and holes. This is achieved by considering the \textit{persistent homology} of a pair of sublevel set functions corresponding to a pair of distance functions defined on the ambient space. The model is applicable to both single and multiple component shapes and, to the authors knowledge, is the first shape model to consider multi-scale topological features of multiple component shapes. It is demonstrated, both qualitatively and quantitatively, that the proposed model models useful multi-scale topological features and outperforms a commonly used benchmark models with respect to the task of multiple component shape retrieval

Persistence codebooks for topological data analysis

Persistent homology is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs) which are 2D multisets of points. Their variable size makes them, however, difficult to combine with typical machine learning workflows. In this paper we introduce persistence codebooks, a novel expressive and discriminative fixed-size vectorized representation of PDs that adapts to the inherent sparsity of persistence diagrams. To this end, we adapt bag-of-words, vectors of locally aggregated descriptors and Fischer vectors for the quantization of PDs. Persistence codebooks represent PDs in a convenient way for machine learning and statistical analysis and have a number of favorable practical and theoretical properties including 1-Wasserstein stability. We evaluate the presented representations on several heterogeneous datasets and show their (high) discriminative power. Our approach yields comparable-and partly even higher-performance in much less time than alternative approaches

A study on topological descriptors for the analysis of 3D surface texture

Methods from computational topology are becoming more and more popular in computer vision and have shown to improve the state-of-the-art in several tasks. In this paper, we investigate the applicability of topological descriptors in the context of 3D surface analysis for the classification of different surface textures. We present a comprehensive study on topological descriptors, investigate their robustness and expressiveness and compare them with state-of-the-art methods including Convolutional Neural Networks (CNNs). Results show that class-specific information is reflected well in topological descriptors. The investigated descriptors can directly compete with non-topological descriptors and capture complementary information. As a consequence they improve the state-of-the-art when combined with non-topological descriptors.Comment: Preprint of Article "A Study on Topological Descriptors for the Analysis of 3D Surface Texture" in Elsevier Journal on Computer Vision and Image Understanding (CVIU): https://doi.org/10.1016/j.cviu.2017.10.012, 17 Pages, 19 Figures, 4 Table