Content-based image retrieval (CBIR) with a static threshold often encounters limitations due to the varying characteristics of queries in different image galleries. In this paper, we propose an approach to address this challenge by introducing dynamic threshold determination for image retrieval. Our method dynamically adjusts the threshold for each gallery based on the distribution of galleries close to that gallery in feature space. By tailoring the threshold to the specific characteristics of each gallery, our approach aims to enhance retrieval accuracy and relevance. We evaluate our method using the ROxford dataset and compare it with the best static threshold. Our approach yields significant enhancements in macro F1 scores across diverse dataset complexities. In the ROxford (medium) scenario, we observed an 12.4% improvement over the static threshold baseline, while in the ROxford (hard) setting, we achieved a 6.41% enhancement. The LogoSearch dataset achieved a 2.72% enhancement.