Using fuzzy similarity measure in content-based video retrieval based on image query
Fatemeh Taheri, Kambiz Rahbar · International Journal of Computational Vision and Robotics · 2025
The primary challenge of video retrieval systems is to retrieve videos with the highest similarity to user queries. The process of feature extraction and similarity measurement plays a crucial role in the results of content-based video retrieval. This article introduces a fuzzy similarity metric for comparing and retrieving similar videos using image-queries to address the issue of uncertainty in the similarity between queries and video frames. To this end, features are extracted from both image-query and each video frame using a pre-trained VGG-16. Similarity metrics, including frequency and continuity in similar frames to the image-query, form the basis for calculating the similarity for retrieving videos. The proposed method compensates for uncertainty in image-query and dataset videos' similarity measurements, leading to improved retrieval results. The best evaluation results with the mean accuracy metric on the UCF-11 dataset for retrieving one and ten top samples are reported as 0.862 and 0.689 respectively.