On Reducing the Number of Visual Words in the Bag-of-Features Representation

TL;DR

This paper proposes techniques to reduce the number of visual words in the Bag of Features model, improving efficiency in image recognition and retrieval without significantly sacrificing accuracy.

Contribution

It introduces and compares methods for reducing visual words in BoF, enhancing efficiency while maintaining recognition performance.

Findings

Significant efficiency improvements in image matching.

Effective reduction of visual words with minimal impact on accuracy.

Enhanced suitability for mobile and large-scale applications.

Abstract

A new class of applications based on visual search engines are emerging, especially on smart-phones that have evolved into powerful tools for processing images and videos. The state-of-the-art algorithms for large visual content recognition and content based similarity search today use the "Bag of Features" (BoF) or "Bag of Words" (BoW) approach. The idea, borrowed from text retrieval, enables the use of inverted files. A very well known issue with this approach is that the query images, as well as the stored data, are described with thousands of words. This poses obvious efficiency problems when using inverted files to perform efficient image matching. In this paper, we propose and compare various techniques to reduce the number of words describing an image to improve efficiency and we study the effects of this reduction on effectiveness in landmark recognition and retrieval scenarios. We show that very relevant improvement in performance are achievable still preserving the advantages of the BoF base approach.