Zero-Shot Learning to Manage a Large Number of Place-Specific Compressive Change Classifiers

TL;DR

This paper introduces a zero-shot learning approach for managing numerous place-specific change classifiers in large-scale, long-term map maintenance, enabling efficient change detection without extensive memorization.

Contribution

It presents a novel zero-shot learning-based change-classifier-learning algorithm that uses external knowledge bases and bag-of-words for efficient, scalable change detection in large maps.

Findings

Efficient management of large numbers of classifiers through training example memorization.

Ability to incorporate new map data by adding or deleting training examples.

Application to a practical long-term, cross-season change detection system.

Abstract

With recent progress in large-scale map maintenance and long-term map learning, the task of change detection on a large-scale map from a visual image captured by a mobile robot has become a problem of increasing criticality. Previous approaches for change detection are typically based on image differencing and require the memorization of a prohibitively large number of mapped images in the above context. In contrast, this study follows the recent, efficient paradigm of change-classifier-learning and specifically employs a collection of place-specific change classifiers. Our change-classifier-learning algorithm is based on zero-shot learning (ZSL) and represents a place-specific change classifier by its training examples mined from an external knowledge base (EKB). The proposed algorithm exhibits several advantages. First, we are required to memorize only training examples (rather than the classifier itself), which can be further compressed in the form of bag-of-words (BoW). Secondly, we can incorporate the most recent map into the classifiers by straightforwardly adding or deleting a few training examples that correspond to these classifiers. Thirdly, we can share the BoW vocabulary with other related task scenarios (e.g., BoW-based self-localization), wherein the vocabulary is generally designed as a rich, continuously growing, and domain-adaptive knowledge base. In our contribution, the proposed algorithm is applied and evaluated on a practical long-term cross-season change detection system that consists of a large number of place-specific object-level change classifiers.