Stopping Rules for Bag-of-Words Image Search and Its Application in Appearance-Based Localization

TL;DR

This paper introduces a difficulty-based stopping rule for bag-of-words image search that reduces computational costs by terminating searches early for easier queries, improving efficiency in appearance-based localization.

Contribution

It presents a novel difficulty measure and stopping rules for BoW image retrieval, optimizing computational resources based on query complexity.

Findings

Significant reduction in search computations for easier queries

Effective application in appearance-based localization

Improved efficiency without sacrificing accuracy

Abstract

We propose a technique to improve the search efficiency of the bag-of-words (BoW) method for image retrieval. We introduce a notion of difficulty for the image matching problems and propose methods that reduce the amount of computations required for the feature vector-quantization task in BoW by exploiting the fact that easier queries need less computational resources. Measuring the difficulty of a query and stopping the search accordingly is formulated as a stopping problem. We introduce stopping rules that terminate the image search depending on the difficulty of each query, thereby significantly reducing the computational cost. Our experimental results show the effectiveness of our approach when it is applied to appearance-based localization problem.