Adaptive Substring Extraction and Modified Local NBNN Scoring for Binary Feature-based Local Mobile Visual Search without False Positives

TL;DR

This paper introduces an adaptive substring extraction and a modified local NBNN scoring method for binary feature-based mobile visual search, significantly improving accuracy and eliminating false positives.

Contribution

It presents a novel adaptive substring extraction, a modified scoring method considering feature density, and a convexity check to prevent false positives in mobile visual search.

Findings

Improves retrieval accuracy by 11% over conventional methods.

Eliminates false positives with the convexity check.

Enhances binary feature matching efficiency.

Abstract

In this paper, we propose a stand-alone mobile visual search system based on binary features and the bag-of-visual words framework. The contribution of this study is three-fold: (1) We propose an adaptive substring extraction method that adaptively extracts informative bits from the original binary vector and stores them in the inverted index. These substrings are used to refine visual word-based matching. (2) A modified local NBNN scoring method is proposed in the context of image retrieval, which considers the density of binary features in scoring each feature matching. (3) In order to suppress false positives, we introduce a convexity check step that imposes a convexity constraint on the configuration of a transformed reference image. The proposed system improves retrieval accuracy by 11% compared with a conventional method without increasing the database size. Furthermore, our system with the convexity check does not lead to false positive results.