CSWS—Suppress confusion subregion in indoor image fingerprint localization

Abstract

AbstractImage data can provide rich content information, which has attracted a lot of attention in the field of indoor fingerprint positioning. However, indoor image information from different locations is characterized by high content repetition, and these repetitive regions can cause the problem of insufficient differentiation of adjacent image fingerprints or even fingerprint misclassification. To solve this problem, this article proposes a confusion subregion weighted suppression strategy in the image fingerprint database. First, duplicate regions (confusion subregions) are extracted from the fingerprint database using an appropriate salient region detection method. Then the similarity of these duplicate regions in Euclidean space is defined, and the degree of influence of these regions on the distinguishability of the fingerprint database is measured. Finally, the suppression of these duplicate regions is achieved in the original image fingerprint database by introducing weighted suppression coefficients. Experiments show that the algorithm proposed in this article can achieve significant results in the fingerprint localization task of real indoor scenes and effectively improve the localization accuracy.