## 2 Proposed Methodology
### Hybrid BoVW-NoM Technique  
This study proposes an image similarity discrimination technique that sequentially applies BoVW- and NoM-based methodologies to achieve an accurate and efficient determination of overlapping images. Specifically, the proposed method integrates the relatively fast processing speed of BoVW with the higher accuracy of NoM, ensuring both speed and accuracy in the image similarity assessment. The proposed method comprises three main stages: (i) image preprocessing, (ii) identification of image set candidates, and (iii) determination of the overlapping image set (Figure 5). First, in the image preprocessing stage, down-sampling was applied to ensure efficiency throughout the entire process. Subsequently, in the candidate selection stage, the BoVW-based rapid exploration of image similarity was utilized to select the image set candidates. Finally, similarities between the selected candidate images and the target were assessed using NoM to determine the overlapping image set. In the preprocessing stage, down-sampling was applied to the input images to reduce their resolution, thereby enhancing the efficiency of the overall image-set selection. UAV images typically have high resolution, resulting in a large number of extracted features. This, in turn, increases the computational workload required for constructing the BoVW codebook and performing feature matching. Moreover, an excessive number of features may lead to redundancy among the features included in the codebook, thereby adversely affecting the efficiency of the similarity search [32]. Therefore, in this study, we aimed to improve the efficiency of image-set determination by decreasing the resolution of the original UAV images. However, excessively low resolutions may result in insufficient features for a similarity search, necessitating judicious determination of the down-sampling level. In this study, the optimal down-sampling level for the dataset was determined to be 25%, based on its impact on processing time and the quality of the overlapping image set results. To identify this optimal level, we conducted preliminary experiments comparing the computational efficiency and accuracy of the proposed overlapping image set selection method using the original images and those down-sampled to 50%, 25%, and 10% of their  
Figure 5: Workflow of the proposed methodology. original dimensions. As shown in Table 3, the total execution time decreased significantly as the size of the images decreased. However, the number of extracted features and image similarity scores decreased significantly. Therefore, identifying accurate overlapping images at a 10% image scale proved to be challenging. Consequently, a down-sampling ratio of 25% was chosen to maintain accuracy while optimizing execution time. In the second stage of the proposed method, feature extraction was conducted on the down-sampled drone images. SURF, a widely utilized method in BoVW-based image retrieval demonstrated in numerous previous studies [33; 34; 35; 36], was employed for extracting image features. SURF efficiently and reliably represents the local features of images, making it widely adopted for BoVW-based image retrieval. Specifically, SURF was implemented with the following parameters: a Hessian threshold of 1000, 3 octaves, and 4 scale levels per octave. As depicted in Figure 6, the features extracted by SURF were utilized in both BoVW codebook generation and NoM feature matching to enhance efficiency. In the third stage of the proposed methodology, image similarity is computed based on the BoVW vector. To calculate image similarity, descriptors of features extracted from the images were initially clustered to form a codebook using clustered centroids. Subsequently, all the images in the dataset were represented as word vectors using the generated codebook. During a similarity search, the image similarity is computed as the cosine similarity between all images in the codebook and the target image. This value ranges from 0 to 1, with higher values indicating greater similarity.