Hierarchical Graphical Models for Multigroup Shape Analysis using Expectation Maximization with Sampling in Kendall's Shape Space

This paper proposes a novel framework for multi-group shape analysis relying on a hierarchical graphical statistical model on shapes within a population.The framework represents individual shapes as point setsmodulo translation, rotation, and scale, following the notion in Kendall shape space.While individual shapes are derived from their group shape model, each group shape model is derived from a single population shape model. The hierarchical model follows the natural organization of population data and the top level in the hierarchy provides a common frame of reference for multigroup shape analysis, e.g. classification and hypothesis testing. Unlike typical shape-modeling approaches, the proposed model is a generative model that defines a joint distribution of object-boundary data and the shape-model variables. Furthermore, it naturally enforces optimal correspondences during the process of model fitting and thereby subsumes the so-called correspondence problem. The proposed inference scheme employs an expectation maximization (EM) algorithm that treats the individual and group shape variables as hidden random variables and integrates them out before estimating the parameters (population mean and variance and the group variances). The underpinning of the EM algorithm is the sampling of pointsets, in Kendall shape space, from their posterior distribution, for which we exploit a highly-efficient scheme based on Hamiltonian Monte Carlo simulation. Experiments in this paper use the fitted hierarchical model to perform (1) hypothesis testing for comparison between pairs of groups using permutation testing and (2) classification for image retrieval. The paper validates the proposed framework on simulated data and demonstrates results on real data.Comment: 9 pages, 7 figures, International Conference on Machine Learning 201

Doctor of Philosophy

dissertationStatistical learning theory has garnered attention during the last decade because it provides the theoretical and mathematical framework for solving pattern recognition problems, such as dimensionality reduction, clustering, and shape analysis. In statis

RESEARCH ON AESTHETIC COGNITION, FLOW EXPERIENCE AND TECHNOLOGY ACCEPTANCE IN VIRTUAL CULTURAL ACTIVITIES

This research explores the integration of aesthetic experience and technology acceptance in virtual cultural activities. Aimed at enhancing user engagement, the study utilizes a structured questionnaire to evaluate participants' aesthetic cognition, flow experiences, and technology acceptance. Methods include data collection from virtual cultural activities and subsequent analytical comparisons. Results indicate a preference for immersive and user-friendly technological interfaces, highlighting the significance of aesthetic cognition in cultural activities' success. The study suggests that improving interactive features could significantly enhance participation in virtual cultural settings