Multimodal Multi-User Surface Recognition with the Kernel Two-Sample Test

TL;DR

This paper introduces a kernel two-sample test-based framework for multimodal surface recognition that automatically detects distribution differences without extensive data tuning, achieving high accuracy on complex datasets.

Contribution

It presents a novel, easy-to-implement data-versus-data approach for multimodal classification that outperforms traditional engineered classifiers in surface recognition tasks.

Findings

Achieved 97.2% accuracy on a 108-class surface dataset.

Outperformed state-of-the-art algorithms by 6% on a challenging task.

Validated the effectiveness of kernel methods for complex pattern recognition.

Abstract

Machine learning and deep learning have been used extensively to classify physical surfaces through images and time-series contact data. However, these methods rely on human expertise and entail the time-consuming processes of data and parameter tuning. To overcome these challenges, we propose an easily implemented framework that can directly handle heterogeneous data sources for classification tasks. Our data-versus-data approach automatically quantifies distinctive differences in distributions in a high-dimensional space via kernel two-sample testing between two sets extracted from multimodal data (e.g., images, sounds, haptic signals). We demonstrate the effectiveness of our technique by benchmarking against expertly engineered classifiers for visual-audio-haptic surface recognition due to the industrial relevance, difficulty, and competitive baselines of this application; ablation studies confirm the utility of key components of our pipeline. As shown in our open-source code, we achieve 97.2% accuracy on a standard multi-user dataset with 108 surface classes, outperforming the state-of-the-art machine-learning algorithm by 6% on a more difficult version of the task. The fact that our classifier obtains this performance with minimal data processing in the standard algorithm setting reinforces the powerful nature of kernel methods for learning to recognize complex patterns.