This paper introduces a novel approach for creating a visual place recognition (VPR) database for localization in indoor environments from RGBD scanning sequences. The proposed method formulates the problem as a minimization challenge by utilizing a dominating set algorithm applied to a graph constructed from spatial information, referred to as the “DominatingSet” algorithm. Experimental results on various datasets, including 7-scenes, BundleFusion, RISEdb, and a specifically recorded sequences in a highly repetitive office setting, demonstrate that our technique significantly reduces database size while maintaining comparable VPR performance to state-of-the-art approaches in challenging environments. Additionally, our solution enables weakly-supervised labeling for all images from the sequences, facilitating the automatic fine-tuning of VPR algorithm to target environment. Additionally, this paper presents a fully automated pipeline for creating VPR databases from RGBD scanning sequences and introduces a set of metrics for evaluating the performance of VPR databases. The code and released data are available on our web-page — https://prime-slam.github.io/place-recognition-db/.