Hyperdimensional computing as a framework for systematic aggregation of image descriptors

TL;DR

This paper introduces a hyperdimensional computing framework for aggregating image descriptors into compact, holistic vectors, improving performance in mobile robotics place recognition tasks.

Contribution

It presents a novel HDC-based method for combining local image descriptors and their positions into a single vector, enhancing aggregation efficiency and accuracy.

Findings

20% performance improvement over existing methods

3.6x better worst-case performance

Effective processing of deep-learning based descriptors

Abstract

Image and video descriptors are an omnipresent tool in computer vision and its application fields like mobile robotics. Many hand-crafted and in particular learned image descriptors are numerical vectors with a potentially (very) large number of dimensions. Practical considerations like memory consumption or time for comparisons call for the creation of compact representations. In this paper, we use hyperdimensional computing (HDC) as an approach to systematically combine information from a set of vectors in a single vector of the same dimensionality. HDC is a known technique to perform symbolic processing with distributed representation in numerical vectors with thousands of dimensions. We present a HDC implementation that is suitable for processing the output of existing and future (deep-learning based) image descriptors. We discuss how this can be used as a framework to process descriptors together with additional knowledge by simple and fast vector operations. A concrete outcome is a novel HDC-based approach to aggregate a set of local image descriptors together with their image positions in a single holistic descriptor. The comparison to available holistic descriptors and aggregation methods on a series of standard mobile robotics place recognition experiments shows a 20% improvement in average performance compared to runner-up and 3.6x better worst-case performance.