Variational Bayesian Logistic Tensor Regression with Application to Image Recognition

TL;DR

This paper introduces a variational Bayesian logistic tensor regression method that leverages tensor decomposition and shrinkage priors to improve image classification accuracy, especially with limited data, demonstrated on flower and X-ray images.

Contribution

It proposes a novel variational Bayesian approach for tensor regression in image recognition, incorporating tensor decomposition and multiway shrinkage priors for better structural data utilization.

Findings

Method achieves high accuracy, precision, and F1 score in simulations.

Effective in classifying flower and chest X-ray images.

Handles tensor data sparsity effectively.

Abstract

In recent years, image recognition method has been a research hotspot in various fields such as video surveillance, biometric identification, unmanned vehicles, human-computer interaction, and medical image recognition. Existing recognition methods often ignore structural information of image data or depend heavily on the sample size of image data. To address this issue, we develop a novel variational Bayesian method for image classification in a logistic tensor regression model with image tensor predictors by utilizing tensor decomposition to approximate tensor regression. To handle the sparsity of tensor coefficients, we introduce the multiway shrinkage priors for marginal factor vectors of tensor coefficients. In particular, we obtain a closed-form approximation to the variational posteriors for classification prediction based on the matricization of tensor decomposition. Simulation studies are conducted to investigate the performance of the proposed methodologies in terms of accuracy, precision and F1 score. Flower image data and chest X-ray image data are illustrated by the proposed methodologies.